diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..df3a62c73b6d39779bb30a191ee2bd54bffd1f84 --- /dev/null +++ b/.gitignore @@ -0,0 +1,18 @@ +# --- SENSITIVE KEYS (Never Push) --- +.env +backend/.env +frontend/.env +*.env + +# --- HEAVY FOLDERS (Never Push) --- +node_modules/ +frontend/node_modules/ +backend/env/ +env/ +__pycache__/ +backend/__pycache__/ +dist/ +build/ + +# --- ML MODELS (Ignore entire folder over 100MB) --- +backend/ml_models/ \ No newline at end of file diff --git a/backend/app.py b/backend/app.py new file mode 100644 index 0000000000000000000000000000000000000000..1cbaac2f078efe6484918a5b450efaf1568778b2 --- /dev/null +++ b/backend/app.py @@ -0,0 +1,106 @@ +# ============================================================= +# FILE: app.py +# Optimized Flask + SocketIO entry (threading mode, no debug) +# ============================================================= +import logging +from flask import Flask, jsonify +from flask_cors import CORS +from flask_socketio import SocketIO + + +# lightweight logging +logging.getLogger('werkzeug').setLevel(logging.ERROR) +logging.getLogger('socketio').setLevel(logging.ERROR) + + +app = Flask(__name__) +CORS(app, resources={r"/api/*": {"origins": "*"}}) + + +# Use threading mode to avoid eventlet monkey-patch issues with Scapy/IO +socketio = SocketIO(app, cors_allowed_origins="*", async_mode="threading") + + +# Mail initialization is left as-is but keep credentials out of source in production +try: + from extensions import mail + app.config.update( + MAIL_SERVER="smtp.gmail.com", + MAIL_PORT=587, + MAIL_USE_TLS=True, + MAIL_USERNAME="yishu2005.ju@gmail.com", + MAIL_PASSWORD="prko cejt awef zmmi", + MAIL_DEFAULT_SENDER=("Adaptive AI NIDS", "yishu2005.ju@gmail.com") + ) + mail.init_app(app) + +except Exception: +# If mail is not available in dev/test, continue gracefully + pass + + +# lazy import of sniffer so import side-effects are minimal +sniffer = None + + +def _get_sniffer(): + global sniffer + if sniffer is None: + from capture.live_manager import sniffer as _s + sniffer = _s + return sniffer + + +# Register blueprints lazily to avoid heavy imports at startup +def register_blueprints(app): + from importlib import import_module + + + routes = [ + ("routes.live_route", "live_bp", "/api/live"), + ("routes.logs_route", "logs_bp", "/api/logs"), + ("routes.predict_route", "predict_bp", "/api/predict"), + ("routes.reports_route", "reports_bp", "/api/reports"), + ("routes.ip_lookup_route", "ip_lookup_bp", "/api/ip"), + ("routes.geo_route", "geo_bp", "/api/geo"), + ("routes.alerts_route", "alerts_bp", "/api"), + ("routes.system_info", "system_bp", "/api"), + ("routes.ml_route", "ml_bp", "/api"), + ("routes.traffic_routes", "traffic_bp", "/api"), + ("routes.ml_switch_route","ml_switch","/api/model"), + ("routes.manual_predict_route","manual_predict","/api"), + ("routes.ai_route","ai_bp","/api/ai"), + ("routes.chat_route","chat_bp","/api"), + ("routes.offline_detection","offline_bp","/api/offline") + ] + + for module_name, varname, prefix in routes: + try: + mod = import_module(module_name) + bp = getattr(mod, varname) + app.register_blueprint(bp, url_prefix=prefix) + print(f"✅ Registered route: {module_name} -> {prefix}") + + except Exception as e: + print(f"⚠️ Skipping {module_name}: {e}") + +register_blueprints(app) + + +@app.route("/") +def home(): + s = _get_sniffer() + return jsonify({ + "status": "✅ Backend Active", + "capture_running": s.is_running() if s else False, + "tip": "Use /api/live/start and /api/live/stop to control capture" + }) + + +if __name__ == "__main__": + print("🚀 Starting Adaptive AI NIDS Backend (threading mode)...") + # Run without debug — debug spawns extra processes and uses more CPU + socketio.run(app, host="0.0.0.0", port=5000, debug=False) + + + diff --git a/backend/capture/__init__.py b/backend/capture/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/backend/capture/live_capture.py b/backend/capture/live_capture.py new file mode 100644 index 0000000000000000000000000000000000000000..c3d6b21ab888c463519efb2392850e78922cef13 --- /dev/null +++ b/backend/capture/live_capture.py @@ -0,0 +1,620 @@ +# backend/capture/live_capture.py +# Flow-aware live capture supporting both BCC (per-packet) and CICIDS (flow-aggregated) +import os +import time +import threading +import queue +from datetime import datetime +from collections import defaultdict, deque +import numpy as np +from scapy.all import sniff, IP, TCP, UDP # keep scapy usage +import joblib + +from utils.logger import push_event +from socket_manager import emit_new_event +from utils.model_selector import get_active_model, load_model + +# ------------------------- +# Tunables +# ------------------------- +CAPTURE_QUEUE_MAX = 5000 +PROCESS_BATCH_SIZE = 40 +EMIT_INTERVAL = 0.5 +BPF_FILTER = "tcp or udp" +SAMPLE_RATE = 0.45 +THROTTLE_PER_PACKET = 0.02 + +# Flow builder tunables +FLOW_IDLE_TIMEOUT = 1.5 # seconds of inactivity -> expire flow +FLOW_PACKET_THRESHOLD = 50 # force flush if many packets +FLOW_MAX_TRACKED = 20000 # limit number of active flows tracked to avoid memory explosion + +# ------------------------- +# Internal state +# ------------------------- +_packet_queue = queue.Queue(maxsize=CAPTURE_QUEUE_MAX) +_running = threading.Event() +_last_emit = 0.0 + +# Flow table and lock +_flows = dict() # flow_key -> Flow object +_flows_lock = threading.Lock() + +# background threads +_processor_thr = None +_capture_thr = None +_expiry_thr = None + +# ------------------------- +# Flow data container +# ------------------------- +class Flow: + def __init__(self, first_pkt, ts): + # 5-tuple key derived externally + self.first_seen = ts + self.last_seen = ts + self.packets_total = 0 + self.packets_fwd = 0 + self.packets_bwd = 0 + self.bytes_fwd = 0 + self.bytes_bwd = 0 + self.fwd_lens = [] # for mean + self.bwd_lens = [] + self.inter_arrivals = [] # global IATs across flow + self.last_pkt_ts = ts + self.fwd_psh = 0 + self.fwd_urg = 0 + self.protocol = 6 if first_pkt.haslayer(TCP) else (17 if first_pkt.haslayer(UDP) else 0) + # store client/server ip+port orientation based on first packet's src/dst + self.client_ip = first_pkt[IP].src + self.server_ip = first_pkt[IP].dst + self.client_port = first_pkt.sport if hasattr(first_pkt, 'sport') else 0 + self.server_port = first_pkt.dport if hasattr(first_pkt, 'dport') else 0 + + def update(self, pkt, ts): + self.packets_total += 1 + # Determine direction relative to initial client/server + try: + src = pkt[IP].src + sport = pkt.sport if hasattr(pkt, 'sport') else 0 + payload = bytes(pkt.payload) if pkt.payload else b"" + plen = len(payload) + except Exception: + src = None; sport = 0; plen = 0 + + # if src equals initial client, it's forward + if src == self.client_ip and sport == self.client_port: + dir_fwd = True + else: + dir_fwd = False + + if dir_fwd: + self.packets_fwd += 1 + self.bytes_fwd += plen + self.fwd_lens.append(plen) + # flags + if pkt.haslayer(TCP): + flags = pkt[TCP].flags + if flags & 0x08: # PSH + self.fwd_psh += 1 + if flags & 0x20: # URG + self.fwd_urg += 1 + else: + self.packets_bwd += 1 + self.bytes_bwd += plen + self.bwd_lens.append(plen) + + # inter-arrival + iat = ts - (self.last_pkt_ts or ts) + if iat > 0: + self.inter_arrivals.append(iat) + self.last_pkt_ts = ts + self.last_seen = ts + + def is_idle(self, now, idle_timeout): + return (now - self.last_seen) >= idle_timeout + + def build_cicids_features(self, dst_port_override=None): + """ + Build feature vector matching: + ['Protocol', 'Dst Port', 'Flow Duration', 'Tot Fwd Pkts', 'Tot Bwd Pkts', + 'TotLen Fwd Pkts', 'TotLen Bwd Pkts', 'Fwd Pkt Len Mean', 'Bwd Pkt Len Mean', + 'Flow IAT Mean', 'Fwd PSH Flags', 'Fwd URG Flags', 'Fwd IAT Mean'] + -> returns list of floats/ints + """ + duration = max(self.last_seen - self.first_seen, 0.000001) + tot_fwd = self.packets_fwd + tot_bwd = self.packets_bwd + totlen_fwd = float(self.bytes_fwd) + totlen_bwd = float(self.bytes_bwd) + fwd_mean = float(np.mean(self.fwd_lens)) if self.fwd_lens else 0.0 + bwd_mean = float(np.mean(self.bwd_lens)) if self.bwd_lens else 0.0 + flow_iat_mean = float(np.mean(self.inter_arrivals)) if self.inter_arrivals else 0.0 + fwd_iat_mean = self._fwd_iat_mean() + proto = int(self.protocol) + # FIXED: respect explicit override even if zero + dst_port = self.server_port if dst_port_override is None else int(dst_port_override or 0) + + return [ + proto, + dst_port, + duration, + tot_fwd, + tot_bwd, + totlen_fwd, + totlen_bwd, + fwd_mean, + bwd_mean, + flow_iat_mean, + self.fwd_psh, + self.fwd_urg, + fwd_iat_mean + ] + + def _fwd_iat_mean(self): + # approximate forward-only IATs by splitting inter_arrivals roughly (coarse) + # If we had per-direction timestamps we would measure precisely; + # here we approximate as global mean when forward packets exist. + if self.inter_arrivals and self.packets_fwd > 0: + return float(np.mean(self.inter_arrivals)) + return 0.0 + +# ------------------------- +# helpers: flow key +# ------------------------- +def make_flow_key(pkt): + try: + ip = pkt[IP] + proto = 6 if pkt.haslayer(TCP) else (17 if pkt.haslayer(UDP) else 0) + sport = pkt.sport if hasattr(pkt, 'sport') else 0 + dport = pkt.dport if hasattr(pkt, 'dport') else 0 + # canonicalize tuple order to consider direction + return (ip.src, ip.dst, sport, dport, proto) + except Exception: + return None + +# ------------------------- +# queueing / sniff simple wrappers +# ------------------------- +def _enqueue(pkt): + try: + _packet_queue.put_nowait((pkt, time.time())) + except queue.Full: + return + +def _packet_capture_worker(iface=None): + sniff(iface=iface, prn=_enqueue, store=False, filter=BPF_FILTER) + +# ------------------------- +# Expiry thread: periodically expire idle flows +# ------------------------- +def _expiry_worker(): + while _running.is_set(): + time.sleep(0.5) + now = time.time() + to_flush = [] + with _flows_lock: + keys = list(_flows.keys()) + for k in keys: + f = _flows.get(k) + if f is None: + continue + if f.is_idle(now, FLOW_IDLE_TIMEOUT) or f.packets_total >= FLOW_PACKET_THRESHOLD: + to_flush.append(k) + + if to_flush: + _process_and_emit_flows(to_flush) + +# ------------------------- +# core: process queue, update flows, flush when needed +# ------------------------- +def _processor_worker(): + global _last_emit + # lazy load initial model bundle + active = get_active_model() + model_bundle = load_model(active) + processor_model = model_bundle.get("model") + processor_scaler = model_bundle.get("scaler") or (model_bundle.get("artifacts") and model_bundle["artifacts"].get("scaler")) + processor_encoder = model_bundle.get("encoder") or (model_bundle.get("artifacts") and model_bundle["artifacts"].get("label_encoder")) + + batch = [] + while _running.is_set(): + # refresh model if switched + new_active = get_active_model() + if new_active != active: + active = new_active + model_bundle = load_model(active) + processor_model = model_bundle.get("model") + processor_scaler = model_bundle.get("scaler") or (model_bundle.get("artifacts") and model_bundle["artifacts"].get("scaler")) + processor_encoder = model_bundle.get("encoder") or (model_bundle.get("artifacts") and model_bundle["artifacts"].get("label_encoder")) + print(f"[live_capture] switched active model to {active}") + + try: + pkt, ts = _packet_queue.get(timeout=0.5) + except queue.Empty: + # flush small batches if exist (not required) + continue + + # sampling, ignore some traffic + if np.random.rand() > SAMPLE_RATE: + continue + if not pkt.haslayer(IP): + continue + + # BCC path: still do per-packet predictions if active 'bcc' + if active == "bcc": + batch.append((pkt, ts)) + if len(batch) >= PROCESS_BATCH_SIZE or _packet_queue.empty(): + _process_bcc_batch(batch, processor_model, processor_scaler, processor_encoder) + batch.clear() + continue + + # CICIDS path: update flow table + key = make_flow_key(pkt) + if key is None: + continue + + # Prevent runaway flows table + with _flows_lock: + if len(_flows) > FLOW_MAX_TRACKED: + # flush oldest flows (heuristic) to free space + # choose keys ordered by last_seen + items = list(_flows.items()) + items.sort(key=lambda kv: kv[1].last_seen) + n_to_remove = int(len(items) * 0.1) or 100 + keys_to_flush = [k for k, _ in items[:n_to_remove]] + # flush asynchronously + threading.Thread(target=_process_and_emit_flows, args=(keys_to_flush,), daemon=True).start() + + flow = _flows.get(key) + if flow is None: + # new flow + flow = Flow(pkt, ts) + _flows[key] = flow + + # update outside big lock (Flow.update is mostly per-flow) + flow.update(pkt, ts) + + # flush immediately if surpass threshold + if flow.packets_total >= FLOW_PACKET_THRESHOLD: + _process_and_emit_flows([key]) + + # when stopped, flush all + with _flows_lock: + keys = list(_flows.keys()) + if keys: + _process_and_emit_flows(keys) + + +# ------------------------- +# Process BCC batch (existing behavior) +# ------------------------- +def _process_bcc_batch(batch, model, scaler, encoder): + events = [] + features_list = [] + for pkt, ts in batch: + # reuse earlier extraction (simple) + features = _extract_bcc_vector(pkt) + features_list.append(features) + + X = np.asarray(features_list, dtype=float) + if scaler is not None: + try: + Xs = scaler.transform(X) + except Exception: + Xs = X + else: + Xs = X + + if model is not None: + try: + preds = model.predict(Xs) + probs = model.predict_proba(Xs) if hasattr(model, "predict_proba") else None + except Exception as e: + preds = [None] * len(Xs) + probs = None + print("[live_capture] BCC model predict failed:", e) + else: + preds = [None] * len(Xs) + probs = None + + for i, (pkt, ts) in enumerate(batch): + pred = preds[i] + conf = float(np.max(probs[i])) if (probs is not None and len(probs) > i) else None + try: + decoded = encoder.inverse_transform([int(pred)])[0] if encoder else str(pred) + except Exception: + decoded = str(pred) + + evt = { + "time": datetime.now().strftime("%H:%M:%S"), + "src_ip": pkt[IP].src, + "dst_ip": pkt[IP].dst, + "sport": (pkt.sport if (pkt.haslayer(TCP) or pkt.haslayer(UDP)) else 0), + "dport": (pkt.dport if (pkt.haslayer(TCP) or pkt.haslayer(UDP)) else 0), + "proto": "TCP" if pkt.haslayer(TCP) else ("UDP" if pkt.haslayer(UDP) else "OTHER"), + "prediction": decoded, + "confidence": conf if conf is None or isinstance(conf, float) else float(conf), + "packet_meta": extract_packet_metadata(pkt) # <-- NEW +} + + try: + push_event(evt) + except Exception: + pass + events.append(evt) + + # emit once per batch + if events: + try: + emit_new_event({"items": events, "count": len(events)}) + except Exception: + pass + + +def _extract_bcc_vector(pkt): + # this matches your old extract_bcc_features but kept minimal and robust + try: + proto = 6 if pkt.haslayer(TCP) else (17 if pkt.haslayer(UDP) else 1) + src_port = pkt.sport if pkt.haslayer(TCP) or pkt.haslayer(UDP) else 0 + dst_port = pkt.dport if pkt.haslayer(TCP) or pkt.haslayer(UDP) else 0 + + payload = bytes(pkt.payload) if pkt.payload else b"" + plen = len(payload) + header = max(len(pkt) - plen, 0) + + syn = 1 if pkt.haslayer(TCP) and pkt[TCP].flags & 0x02 else 0 + ack = 1 if pkt.haslayer(TCP) and pkt[TCP].flags & 0x10 else 0 + rst = 1 if pkt.haslayer(TCP) and pkt[TCP].flags & 0x04 else 0 + fin = 1 if pkt.haslayer(TCP) and pkt[TCP].flags & 0x01 else 0 + + return [ + proto, + src_port, + dst_port, + 0.001, + 1, + 1, + 0, + plen, + header, + plen / 0.002 if 0.002 else plen, + 1 / 0.002 if 0.002 else 1, + syn, + ack, + rst, + fin + ] + except Exception: + return [0] * 15 + + +# ------------------------- +# Packet-level metadata extractor +# ------------------------- +def extract_packet_metadata(pkt): + """Extract detailed packet-level metadata for frontend display.""" + meta = {} + + # IP-level metadata + try: + meta["ttl"] = pkt[IP].ttl if pkt.haslayer(IP) else None + meta["pkt_len"] = len(pkt) + except: + meta["ttl"] = None + meta["pkt_len"] = None + + # TCP metadata + if pkt.haslayer(TCP): + tcp = pkt[TCP] + try: + meta["seq"] = int(tcp.seq) + meta["ack"] = int(tcp.ack) + meta["window"] = int(tcp.window) + meta["flags"] = str(tcp.flags) + meta["header_len"] = tcp.dataofs * 4 # Data offset (words) + except: + meta["seq"] = None + meta["ack"] = None + meta["window"] = None + meta["flags"] = None + meta["header_len"] = None + else: + meta["seq"] = None + meta["ack"] = None + meta["window"] = None + meta["flags"] = None + meta["header_len"] = None + + # Payload length + try: + payload = bytes(pkt.payload) + meta["payload_len"] = len(payload) + except: + meta["payload_len"] = None + + return meta + +# ------------------------- +# flush flows and emit/predict +# ------------------------- +def _process_and_emit_flows(keys): + # keys: list of flow_keys to flush; safe to call from any thread + # collect features for predict, delete flows + to_predict = [] + mapping = [] # keep (flow_key, flow_obj) for events + with _flows_lock: + for k in keys: + f = _flows.pop(k, None) + if f: + mapping.append((k, f)) + + if not mapping: + return + + # create features list + for k, f in mapping: + feat = f.build_cicids_features() + to_predict.append((k, f, feat)) + + X = np.array([t[2] for t in to_predict], dtype=float) + # lazy load latest model bundle (in case switching) + active = get_active_model() + bundle = load_model(active) + model = bundle.get("model") + scaler = None + artifacts = bundle.get("artifacts") + + # try to get scaler from bundle/artifacts + if bundle.get("scaler") is not None: + scaler = bundle.get("scaler") + elif artifacts and artifacts.get("scaler") is not None: + scaler = artifacts.get("scaler") + + if scaler is not None: + try: + # If scaler expects dataframe shape, it should still accept ndarray + Xs = scaler.transform(X) + except Exception as e: + print("[live_capture] cicids scaler transform failed:", e) + Xs = X + else: + Xs = X + + preds = [] + probs = None + if model is not None: + try: + preds = model.predict(Xs) + if hasattr(model, "predict_proba"): + try: + probs = model.predict_proba(Xs) + except Exception: + probs = None + except Exception as e: + print("[live_capture] cicids model predict failed:", e) + preds = [None] * len(Xs) + probs = None + else: + preds = [None] * len(Xs) + + # build events and emit/push + events = [] + for i, (k, f, feat) in enumerate(to_predict): + pred = preds[i] + conf = float(np.max(probs[i])) if (probs is not None and len(probs) > i) else None + + # ------------------------- + # SIMPLIFIED LABEL DECODING + # ------------------------- + # Your RF pipeline outputs string labels directly (e.g. 'DoS attacks-Hulk', 'BENIGN'). + # So keep it simple and safe: + try: + label = str(pred) + except Exception: + label = repr(pred) + + evt = { + "time": datetime.now().strftime("%H:%M:%S"), + "src_ip": f.client_ip, + "dst_ip": f.server_ip, + "sport": f.client_port, + "dport": f.server_port, + "proto": "TCP" if f.protocol == 6 else ("UDP" if f.protocol == 17 else "OTHER"), + "prediction": label, + "confidence": conf if conf is None or isinstance(conf, float) else float(conf), + "features": feat, + "flow_summary": { + "packets_fwd": f.packets_fwd, + "packets_bwd": f.packets_bwd, + "bytes_fwd": f.bytes_fwd, + "bytes_bwd": f.bytes_bwd, + "duration": f.last_seen - f.first_seen, + "fwd_mean_len": float(np.mean(f.fwd_lens)) if f.fwd_lens else 0.0 + } +} + + try: + push_event(evt) + except Exception: + pass + events.append(evt) + + if events: + try: + emit_new_event({"items": events, "count": len(events)}) + except Exception: + pass + +# ------------------------- +# start/stop API (keeps your old signatures) +# ------------------------- +def start_live_capture_packet_mode(iface=None): + """Start packet capture + processor + expiry threads.""" + global _processor_thr, _capture_thr, _expiry_thr + if _running.is_set(): + print("Already running") + return + _running.set() + _processor_thr = threading.Thread(target=_processor_worker, daemon=True) + _capture_thr = threading.Thread(target=_packet_capture_worker, kwargs={"iface": iface}, daemon=True) + _expiry_thr = threading.Thread(target=_expiry_worker, daemon=True) + _processor_thr.start() + _capture_thr.start() + _expiry_thr.start() + print("Live capture started (flow-aware)") + +def stop_live_capture(): + _running.clear() + time.sleep(0.2) + # flush all flows and stop + with _flows_lock: + keys = list(_flows.keys()) + if keys: + _process_and_emit_flows(keys) + print("Stopping capture...") + +def is_running(): + return _running.is_set() + +# ------------------------- +# Small test helpers (simulate simple flow packets) +# ------------------------- +def _make_fake_pkt(src, dst, sport, dport, proto='TCP', payload_len=100, flags=0x18): + """Return a tiny object resembling scapy packet for testing without scapy.""" + # If scapy present prefer to build actual IP/TCP + try: + if proto.upper() == 'TCP': + from scapy.all import IP, TCP + pkt = IP(src=src, dst=dst)/TCP(sport=sport, dport=dport, flags=flags)/("X"*payload_len) + return pkt + elif proto.upper() == 'UDP': + from scapy.all import IP, UDP + pkt = IP(src=src, dst=dst)/UDP(sport=sport, dport=dport)/("X"*payload_len) + return pkt + except Exception: + # fallback plain namespace + class SimplePkt: + def __init__(self): + self.payload = b"X"*payload_len + self.len = payload_len + 40 + def haslayer(self, cls): + return False + return SimplePkt() + +def simulate_flow(src="10.0.0.1", dst="10.0.0.2", sport=1234, dport=80, count=6, interval=0.1): + """Quick local simulator: pushes `count` fake packets for a flow into the queue.""" + for i in range(count): + pkt = _make_fake_pkt(src, dst, sport, dport, proto='TCP', payload_len=100, flags=0x18) + _enqueue((pkt, time.time())) if False else _packet_queue.put_nowait((pkt, time.time())) + time.sleep(interval) + +# ---------------------------------------------------------------------------- +# If you want to test this module interactively: +# 1) from backend.capture import live_capture +# 2) live_capture.start_live_capture_packet_mode() +# 3) call live_capture.simulate_flow(...) or send real packets +# 4) view server logs, or GET /api/live/recent to see events (existing route) +# ---------------------------------------------------------------------------- + + diff --git a/backend/capture/live_manager.py b/backend/capture/live_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..28e52e4a21dff42fe38409e52e2c7379c498308b --- /dev/null +++ b/backend/capture/live_manager.py @@ -0,0 +1,57 @@ +# live_manager.py (Optimized) +# ------------------------------------------------------------- +import threading +import time +from typing import Optional +from .live_capture import start_live_capture_packet_mode, stop_live_capture, is_running +from utils.logger import get_recent_events, get_model_stats, get_active_model + + +class LiveSniffer: + def __init__(self): + self._thr: Optional[threading.Thread] = None + self._lock = threading.Lock() + self._iface = None + self._last_start_time = None + + def start(self, iface=None, packet_limit=0): + with self._lock: + if is_running(): + print("Already running.") + return + self._iface = iface + self._last_start_time = time.strftime("%H:%M:%S") + + def _worker(): + print(f"LiveSniffer started on interface={iface or 'default'}") + try: + # FIX: start_live_capture_packet_mode signature accepts iface only + start_live_capture_packet_mode(iface=self._iface) + except Exception as e: + print("Sniffer error:", e) + print("LiveSniffer thread exit.") + + self._thr = threading.Thread(target=_worker, daemon=True) + self._thr.start() + + def stop(self): + with self._lock: + if not is_running(): + print("Already stopped.") + return + stop_live_capture() + + if self._thr and self._thr.is_alive(): + self._thr.join(timeout=3) + print("Sniffer fully stopped.") + + def is_running(self) -> bool: + return is_running() + + + def recent(self, n=200): + return get_recent_events(get_active_model(), n) + def stats(self): + return get_model_stats(get_active_model()) + +sniffer = LiveSniffer() diff --git a/backend/extensions.py b/backend/extensions.py new file mode 100644 index 0000000000000000000000000000000000000000..2a66feb79daededb865f1497a2862bc268a7360e --- /dev/null +++ b/backend/extensions.py @@ -0,0 +1,4 @@ +# backend/extensions.py +from flask_mail import Mail + +mail = Mail() diff --git a/backend/flow_builder.py b/backend/flow_builder.py new file mode 100644 index 0000000000000000000000000000000000000000..ebd73f4d218e42a4e8ecec5746504420b1850947 --- /dev/null +++ b/backend/flow_builder.py @@ -0,0 +1,35 @@ +# flow_builder.py +from collections import defaultdict + +def build_flows(events): + flows = defaultdict(lambda: { + "src_ip": "", + "dst_ip": "", + "sport": "", + "dport": "", + "proto": "", + "packets": 0, + "bytes": 0, + "first_seen": "", + "last_seen": "", + }) + + for e in events: + key = (e["src_ip"], e["dst_ip"], e["sport"], e["dport"], e["proto"]) + f = flows[key] + + f["src_ip"] = e["src_ip"] + f["dst_ip"] = e["dst_ip"] + f["sport"] = e["sport"] + f["dport"] = e["dport"] + f["proto"] = e["proto"] + + f["packets"] += 1 + f["bytes"] += 1500 # approximation (or use real payload length if available) + + # Update timestamps + if not f["first_seen"]: + f["first_seen"] = e.get("time") + f["last_seen"] = e.get("time") + + return list(flows.values()) diff --git a/backend/generated_reports/traffic_logs.csv b/backend/generated_reports/traffic_logs.csv new file mode 100644 index 0000000000000000000000000000000000000000..6dbc0ec2d0069da2af5d1e2b36f87b9c73a234cb --- /dev/null +++ b/backend/generated_reports/traffic_logs.csv @@ -0,0 +1,8 @@ +date,VPN,TOR,I2P,DDoS +2025-11-06,3,1,0,5 +2025-11-07,4,3,1,7 +2025-11-08,7,2,0,6 +2025-11-09,5,4,2,8 +2025-11-10,2,3,1,4 +2025-11-11,6,2,1,6 +2025-11-12,3,1,0,5 diff --git a/backend/list_groq_models.py b/backend/list_groq_models.py new file mode 100644 index 0000000000000000000000000000000000000000..d3c711efba44e7ccfd01881a29f46ec4da6836ee --- /dev/null +++ b/backend/list_groq_models.py @@ -0,0 +1,13 @@ +import os +import requests + +API_KEY = os.getenv("GROQ_API_KEY") +if not API_KEY: + raise RuntimeError("Set env GROQ_API_KEY") + +resp = requests.get( + "https://api.groq.com/v1/models", + headers={"Authorization": f"Bearer {API_KEY}"} +) +print("Status:", resp.status_code) +print("Response:", resp.text) diff --git a/backend/logs/bcc_logs.csv b/backend/logs/bcc_logs.csv new file mode 100644 index 0000000000000000000000000000000000000000..67c898beaac08a5a973ab7c66724f52a04787ed4 --- /dev/null +++ b/backend/logs/bcc_logs.csv @@ -0,0 +1,3348 @@ +time,src_ip,sport,dst_ip,dport,proto,prediction,risk_level,risk_score,src_country,src_city,src_lat,src_lon,dst_country,dst_city,dst_lat,dst_lon 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0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/backend/reporting/pdf_report.py b/backend/reporting/pdf_report.py new file mode 100644 index 0000000000000000000000000000000000000000..c86a4a81fa81d0692a5c6bd8806aa14044e9cd3b --- /dev/null +++ b/backend/reporting/pdf_report.py @@ -0,0 +1,58 @@ +from fpdf import FPDF +import pandas as pd, os +from utils.logger import log_path + +class NIDSReportPDF(FPDF): + def header(self): + self.set_font("Helvetica", "B", 18) + self.cell(0, 10, "NIDS - Network Intrusion Detection Report", ln=True, align="C") + self.ln(5) + + def footer(self): + self.set_y(-15) + self.set_font("Helvetica", "I", 9) + self.cell(0, 10, f"Page {self.page_no()}/{{nb}}", align="C") + +def generate_pdf_bytes(n=300): + """Generate PDF summary of recent events.""" + df = pd.read_csv(log_path) if os.path.exists(log_path) else pd.DataFrame() + + pdf = NIDSReportPDF() + pdf.alias_nb_pages() + pdf.add_page() + pdf.set_font("Helvetica", "", 12) + + pdf.cell(0, 10, f"Last {n} Events Summary", ln=True) + pdf.ln(5) + + if len(df) == 0: + pdf.cell(0, 10, "No data available.", ln=True) + else: + df = df.tail(n) + counts = df["prediction"].value_counts().to_dict() if "prediction" in df.columns else {} + + pdf.cell(0, 10, "Prediction Distribution:", ln=True) + pdf.ln(4) + for label, count in counts.items(): + pdf.cell(0, 10, f"{label}: {count}", ln=True) + + pdf.ln(8) + pdf.cell(0, 10, "Sample Events:", ln=True) + pdf.ln(4) + + # limit to 10 sample rows + cols = ["time", "src", "dst", "proto", "prediction"] + cols = [c for c in cols if c in df.columns] + for _, row in df.tail(10).iterrows(): + line = " | ".join(str(row[c]) for c in cols) + if len(line) > 150: + line = line[:147] + "..." + pdf.multi_cell(0, 8, line) + + # return as bytes + output = pdf.output(dest="S") + if isinstance(output, (bytes, bytearray)): + return bytes(output) + else: + return bytes(output.encode("latin1", "ignore")) + diff --git a/backend/requirements.txt b/backend/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..da4eb335eda4bc312118f8a454d907d7f4043b06 --- /dev/null +++ b/backend/requirements.txt @@ -0,0 +1,34 @@ +appdirs==1.4.4 +blinker==1.9.0 +click==8.3.0 +colorama==0.4.6 +contourpy==1.3.3 +cycler==0.12.1 +Flask==3.1.2 +flask-cors==6.0.1 +fonttools==4.60.1 +itsdangerous==2.2.0 +Jinja2==3.1.6 +joblib==1.5.2 +kiwisolver==1.4.9 +lightgbm==4.6.0 +lxml==6.0.2 +MarkupSafe==3.0.3 +matplotlib==3.10.7 +numpy==2.3.4 +packaging==25.0 +pandas==2.3.3 +pillow==12.0.0 +pyparsing==3.2.5 +pyshark==0.6 +python-dateutil==2.9.0.post0 +pytz==2025.2 +scapy==2.6.1 +scikit-learn==1.7.2 +scipy==1.16.3 +seaborn==0.13.2 +six==1.17.0 +termcolor==3.2.0 +threadpoolctl==3.6.0 +tzdata==2025.2 +Werkzeug==3.1.3 diff --git a/backend/retrain_requests.jsonl b/backend/retrain_requests.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..473e62655eb3186d77d8d6686ed1d5de211bdd2c --- /dev/null +++ b/backend/retrain_requests.jsonl @@ -0,0 +1 @@ +{"model": "cicids", "expected": "Brute Force -XSS", "predicted": "DoS attacks-Slowloris", "values": [6, 80, 5000000, 2, 2, 120, 120, 60, 60, 2000000, 0, 0, 2000000], "note": "Model is wrong"} diff --git a/backend/routes/__init__.py b/backend/routes/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/backend/routes/ai_route.py b/backend/routes/ai_route.py new file mode 100644 index 0000000000000000000000000000000000000000..87a75d729696a5644348e4a99a277bfc2a869265 --- /dev/null +++ b/backend/routes/ai_route.py @@ -0,0 +1,40 @@ +# routes/ai_route.py +# -------------------------------------- +from flask import Blueprint, request, jsonify +from utils.ai_engine import explain_threat, summarize_events +from utils.logger import get_recent_events, get_active_model + +ai_bp = Blueprint("ai_bp", __name__) + + +@ai_bp.route("/explain", methods=["POST"]) +def ai_explain(): + """ + Body: JSON event (one row from table) + Returns: {"explanation": "..."} + """ + data = request.get_json() or {} + try: + text = explain_threat(data) + return jsonify({"ok": True, "explanation": text}) + except Exception as e: + print("AI explain error:", e) + return jsonify({"ok": False, "error": str(e)}), 500 + + +@ai_bp.route("/summary", methods=["GET"]) +def ai_summary(): + """ + Query: ?model=bcc&n=200 + Returns: {"ok": True, "summary": "..."} + """ + model = request.args.get("model", get_active_model()) + n = int(request.args.get("n", 200)) + + try: + events = get_recent_events(model, n) + text = summarize_events(events, model=model) + return jsonify({"ok": True, "summary": text, "count": len(events), "model": model}) + except Exception as e: + print("AI summary error:", e) + return jsonify({"ok": False, "error": str(e)}), 500 diff --git a/backend/routes/alerts_route.py b/backend/routes/alerts_route.py new file mode 100644 index 0000000000000000000000000000000000000000..ce8e5367b1c055d4265a95c19ca12844840af51b --- /dev/null +++ b/backend/routes/alerts_route.py @@ -0,0 +1,82 @@ +from flask import Blueprint, jsonify +from flask_cors import cross_origin +from utils.logger import get_recent_events +from utils.risk_engine import compute_risk_score +from datetime import datetime + +alerts_bp = Blueprint("alerts", __name__) + +# --------------------------------------------------------- +# Deduce risk based on prediction (simple + stable) +# --------------------------------------------------------- +def classify_risk(prediction): + if prediction in ["TOR", "I2P", "ZERONET", "FREENET"]: + return "High" + if prediction in ["VPN"]: + return "Medium" + return "Low" + + +@alerts_bp.route("/alerts", methods=["GET"]) +@cross_origin() +def get_alerts(): + """ + Returns ONLY real alerts (Medium + High) + with stable risk scoring and time sorting. + Fully compatible with optimized logger. + """ + try: + raw_events = get_recent_events() + alerts = [] + + for e in raw_events: + pred = e.get("prediction", "Unknown") + + # ------------------------------- + # Recompute Risk + # ------------------------------- + risk = classify_risk(pred) + + if risk == "Low": + continue # do NOT include normal traffic + + # ------------------------------- + # Stable risk score (0-100) + # ------------------------------- + try: + risk_score = compute_risk_score(e) + except: + # fallback scoring + risk_score = 90 if risk == "High" else 60 + + # ------------------------------- + # Build alert payload + # ------------------------------- + alerts.append({ + "timestamp": datetime.now().strftime("%H:%M:%S"), + "time": e.get("time"), + "src_ip": e.get("src_ip"), + "dst_ip": e.get("dst_ip"), + "sport": e.get("sport", "—"), + "dport": e.get("dport", "—"), + "proto": e.get("proto", "-"), + "prediction": pred, + "risk_level": risk, + "risk_score": risk_score, + }) + + # ------------------------------------------------ + # Sort newest first (based on event time) + # ------------------------------------------------ + alerts = sorted(alerts, key=lambda x: x["time"], reverse=True) + + return jsonify({ + "count": len(alerts), + "alerts": alerts[:150], # limit for UI performance + "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S") + }) + + except Exception as err: + print("❌ Alerts API error:", err) + return jsonify({"error": str(err)}), 500 + diff --git a/backend/routes/chat_route.py b/backend/routes/chat_route.py new file mode 100644 index 0000000000000000000000000000000000000000..4421fed5f7332a5222c21a616b1904e6117ceb54 --- /dev/null +++ b/backend/routes/chat_route.py @@ -0,0 +1,26 @@ +from flask import Blueprint, request, jsonify +from groq import Groq +import os + +chat_bp = Blueprint("chat_bp", __name__) +client = Groq(api_key=os.getenv("GROQ_API_KEY")) + +@chat_bp.route("/chat", methods=["POST"]) +def chat(): + try: + data = request.get_json() + msg = data.get("message", "") + + result = client.chat.completions.create( + model="llama-3.1-8b-instant", + messages=[{"role": "user", "content": msg}] + ) + + reply = result.choices[0].message.content + + return jsonify({"reply": reply}) + + except Exception as e: + print("Chat error:", e) + return jsonify({"error": str(e)}), 500 + diff --git a/backend/routes/geo_route.py b/backend/routes/geo_route.py new file mode 100644 index 0000000000000000000000000000000000000000..066c7e92b016855c6d5c42e918d0fb53dfacf5ff --- /dev/null +++ b/backend/routes/geo_route.py @@ -0,0 +1,33 @@ +# ========================================== +# 🌍 GEO ROUTE — Adaptive AI NIDS +# ------------------------------------------ +# ✅ /api/geo/resolve?ip= +# ✅ /api/geo/recent +# ========================================== + +from flask import Blueprint, jsonify, request +from utils.geo_lookup import get_geo_info, enrich_event_with_geo +from utils.logger import get_recent_events + +geo_bp = Blueprint("geo", __name__) + +# 🔹 Resolve a single IP (for IPInfoModal) +@geo_bp.route("/resolve") +def resolve_ip(): + ip = request.args.get("ip") + if not ip: + return jsonify({"error": "Missing IP parameter"}), 400 + info = get_geo_info(ip) + return jsonify(info), 200 + + +# 🔹 Return recent events enriched with geo (for map) +@geo_bp.route("/recent") +def geo_recent(): + try: + events = get_recent_events() + geo_events = [enrich_event_with_geo(e) for e in events[-200:]] + return jsonify(geo_events), 200 + except Exception as e: + print("⚠️ Geo recent error:", e) + return jsonify({"error": str(e)}), 500 diff --git a/backend/routes/ip_lookup_route.py b/backend/routes/ip_lookup_route.py new file mode 100644 index 0000000000000000000000000000000000000000..658400fa8fba112633eee70169cc19ee0af31c49 --- /dev/null +++ b/backend/routes/ip_lookup_route.py @@ -0,0 +1,148 @@ +import requests +import ipaddress +from flask import Blueprint, jsonify, request +from flask_cors import cross_origin +from datetime import datetime + +ip_lookup_bp = Blueprint("ip_lookup", __name__) + +# 🔹 Cache lookups to reduce API load +_ip_cache = {} + +# ====================================== +# 🚨 RISK CLASSIFIER +# ====================================== +def _guess_risk(org_name: str): + org = (org_name or "").lower() + if any(k in org for k in ["tor", "anonym", "i2p"]): + return {"level": "High", "score": 95, "reason": "Anonymizing service (TOR/I2P detected)"} + if any(k in org for k in ["vpn", "proxy", "tunnel"]): + return {"level": "Medium", "score": 80, "reason": "VPN or proxy-based routing"} + if any(k in org for k in ["aws", "gcp", "digitalocean", "azure", "oracle"]): + return {"level": "Medium", "score": 70, "reason": "Cloud-hosted server (possible C2 or proxy)"} + return {"level": "Low", "score": 40, "reason": "Likely clean residential or enterprise IP"} + + +# ====================================== +# ⚙️ IP DATA NORMALIZATION +# ====================================== +def _normalize_data(ip, d: dict, api_source: str): + """Unify structure across ipapi.co and ipwho.is""" + if not d: + return {"error": "No data"} + + try: + if api_source == "ipapi": + org = d.get("org", "") + return { + "ip": ip, + "city": d.get("city"), + "region": d.get("region"), + "country_name": d.get("country_name"), + "continent_code": d.get("continent_code"), + "org": org, + "asn": d.get("asn"), + "version": d.get("version", "IPv4"), + "latitude": float(d.get("latitude", 0)), + "longitude": float(d.get("longitude", 0)), + "timezone": d.get("timezone"), + "risk": _guess_risk(org), + "flag": f"https://flagsapi.com/{d.get('country_code','US')}/flat/32.png" + } + elif api_source == "ipwhois": + org = d.get("connection", {}).get("isp", "") + return { + "ip": ip, + "city": d.get("city"), + "region": d.get("region"), + "country_name": d.get("country"), + "continent_code": d.get("continent"), + "org": org, + "asn": d.get("connection", {}).get("asn"), + "version": d.get("type", "IPv4"), + "latitude": float(d.get("latitude", 0)), + "longitude": float(d.get("longitude", 0)), + "timezone": d.get("timezone"), + "risk": _guess_risk(org), + "flag": f"https://flagsapi.com/{d.get('country_code','US')}/flat/32.png" + } + except Exception: + pass + + return {"error": "Normalization failed"} + + +# ====================================== +# 🔍 LOOKUP (PRIVATE or PUBLIC) +# ====================================== +def lookup_ip_data(ip: str): + """Internal helper for backend components (non-JSON).""" + try: + if not ip: + return {"error": "Empty IP"} + + # Check cache first + if ip in _ip_cache: + return _ip_cache[ip] + + # Handle local/private IPs + if ipaddress.ip_address(ip).is_private: + info = { + "ip": ip, + "city": "Bengaluru", + "region": "Private Range", + "country_name": "India", + "org": "Local Device", + "asn": "LAN", + "version": "IPv4", + "latitude": 12.9716, + "longitude": 77.5946, + "risk": {"level": "Low", "score": 20, "reason": "Private/local IP"}, + "flag": "https://flagsapi.com/IN/flat/32.png" + } + _ip_cache[ip] = info + return info + + # === Try ipapi.co === + try: + r = requests.get(f"https://ipapi.co/{ip}/json/", timeout=4) + if r.ok: + d = r.json() + if not d.get("error"): + info = _normalize_data(ip, d, "ipapi") + _ip_cache[ip] = info + return info + except Exception: + pass + + # === Fallback: ipwho.is === + try: + r = requests.get(f"https://ipwho.is/{ip}", timeout=4) + d = r.json() + if d.get("success"): + info = _normalize_data(ip, d, "ipwhois") + _ip_cache[ip] = info + return info + except Exception: + pass + + except Exception as e: + return {"error": str(e)} + + return {"error": "Could not fetch IP info"} + + +# ====================================== +# 🌍 EXTERNAL API ENDPOINT +# ====================================== +@ip_lookup_bp.route("/lookup/", methods=["GET"]) +@cross_origin() +def lookup_ip(ip): + """Public API: Look up an IP's geolocation + threat risk.""" + data = lookup_ip_data(ip) + if "error" in data: + return jsonify(data), 404 + + data["lookup_time"] = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + return jsonify(data) + diff --git a/backend/routes/live_route.py b/backend/routes/live_route.py new file mode 100644 index 0000000000000000000000000000000000000000..a77b288d9cca6fd58750bdef1423925a1f683f83 --- /dev/null +++ b/backend/routes/live_route.py @@ -0,0 +1,53 @@ +# ============================================================== +# live_route.py — Flask routes for controlling live capture +# ============================================================== + +from flask import Blueprint, jsonify, request +from capture.live_manager import sniffer +import numpy as np +import math + + +live_bp = Blueprint("live_bp", __name__) + +@live_bp.route("/start") +def start_live(): + iface = request.args.get("iface") + sniffer.start(iface=iface) + return jsonify({"status": "started", "running": sniffer.is_running()}) + +@live_bp.route("/stop") +def stop_live(): + sniffer.stop() + return jsonify({"status": "stopped", "running": sniffer.is_running()}) + +@live_bp.route("/status") +def status(): + return jsonify({"running": sniffer.is_running()}) + +@live_bp.route("/recent") +def recent(): + events = sniffer.recent() + + safe_events = [] + for e in events: + safe = {} + for k, v in e.items(): + + # convert numpy ints/floats to python native + if isinstance(v, (np.generic,)): + v = v.item() + + # replace None / NaN with string + if v is None or (isinstance(v, float) and math.isnan(v)): + v = "Unknown" + + safe[str(k)] = v + + safe_events.append(safe) + + return jsonify({"events": safe_events}), 200 + +@live_bp.route("/stats") +def stats(): + return jsonify(sniffer.stats()) diff --git a/backend/routes/logs_route.py b/backend/routes/logs_route.py new file mode 100644 index 0000000000000000000000000000000000000000..492d5217e923fbc98391dabd043fd408aabc4f94 --- /dev/null +++ b/backend/routes/logs_route.py @@ -0,0 +1,137 @@ +from flask import Blueprint, send_file, jsonify, request +import os +from utils.logger import ( + BCC_LOG_FILE, + CICIDS_LOG_FILE, + LOG_FILE, + get_recent_events, + get_model_stats, + clear_last_events, + delete_by_prediction, + delete_by_index, + get_active_model +) + +logs_bp = Blueprint("logs", __name__) + + +# ------------------------------- +# DOWNLOAD CSV LOG FILE (global) +# ------------------------------- +@logs_bp.route("/download", methods=["GET"]) +def download_logs(): + model = request.args.get("model") + + # MODEL-SPECIFIC CSVs + if model == "bcc": + path = BCC_LOG_FILE + elif model == "cicids": + path = CICIDS_LOG_FILE + else: + # fallback — global CSV + path = LOG_FILE + + if not os.path.exists(path): + return jsonify({"error": "Log file not found"}), 404 + + return send_file( + path, + as_attachment=True, + download_name=f"{model}_logs.csv" if model else "traffic_logs.csv", + mimetype="text/csv", + ) + + + +# ------------------------------- +# DOWNLOAD MODEL-SPECIFIC JSON +# ------------------------------- +@logs_bp.route("/download/json", methods=["GET"]) +def download_json_logs(): + try: + model = request.args.get("model", get_active_model()) + events = get_recent_events(model) + return jsonify({"model": model, "count": len(events), "events": events}) + except Exception as e: + print("❌ JSON log fetch error:", e) + return jsonify({"error": "Failed to fetch logs"}), 500 + + +# ------------------------------- +# CLEAR MODEL-WISE LAST N EVENTS +# ------------------------------- +@logs_bp.route("/clear", methods=["POST"]) +def clear_logs(): + try: + model = request.args.get("model", get_active_model()) + n = int(request.args.get("n", 50)) + + clear_last_events(model, n) + + print(f"🧹 Cleared last {n} events for model={model}") + return jsonify({"status": "ok", "deleted": n, "model": model}) + except Exception as e: + print("❌ Clear logs error:", e) + return jsonify({"error": str(e)}), 500 + + +# ------------------------------- +# CLEAR MODEL-WISE BY PREDICTION +# ------------------------------- +@logs_bp.route("/clear_pred", methods=["POST"]) +def clear_pred(): + pred = request.args.get("pred") + model = request.args.get("model", get_active_model()) + + if not pred: + return jsonify({"error": "Missing 'pred' parameter"}), 400 + + try: + delete_by_prediction(model, pred) + print(f"🧹 Deleted all events for prediction={pred} in model={model}") + return jsonify({"status": "ok", "deleted_pred": pred, "model": model}) + except Exception as e: + print("❌ Clear prediction error:", e) + return jsonify({"error": str(e)}), 500 + + +# ------------------------------- +# DELETE ONE ROW MODEL-WISE +# ------------------------------- +@logs_bp.route("/delete_one", methods=["POST"]) +def delete_one(): + try: + model = request.args.get("model", get_active_model()) + idx = int(request.args.get("index", -1)) + + ok = delete_by_index(model, idx) + + if ok: + print(f"🗑️ Deleted row index={idx} from model={model}") + return jsonify({"status": "ok", "index": idx, "model": model}) + else: + return jsonify({"status": "invalid index", "index": idx}), 400 + except Exception as e: + print("❌ Delete row error:", e) + return jsonify({"error": str(e)}), 500 + + +# ------------------------------- +# MODEL-WISE LOG STATUS +# ------------------------------- +@logs_bp.route("/status", methods=["GET"]) +def log_status(): + try: + model = request.args.get("model", get_active_model()) + counts = get_model_stats(model) + total = sum(counts.values()) + + return jsonify({ + "model": model, + "total_events": total, + "by_class": counts + }) + except Exception as e: + print("❌ Log status error:", e) + return jsonify({"error": str(e)}), 500 + diff --git a/backend/routes/manual_predict_route.py b/backend/routes/manual_predict_route.py new file mode 100644 index 0000000000000000000000000000000000000000..5efac9add1e2ae7a742bbfc5f3d7bf2442c99beb --- /dev/null +++ b/backend/routes/manual_predict_route.py @@ -0,0 +1,384 @@ +from flask import Blueprint, request, jsonify +from utils.model_selector import get_active_model, load_model +import numpy as np +import traceback +import math +import json + +manual_predict = Blueprint("manual_predict", __name__) + + +def _reliability_score_from_count(count): + # simple monotonic score: log-scale so diminishing returns for many samples + # returns 0-100 + if count is None: + return None + try: + c = float(count) + score = 20 + min(75, math.log10(c + 1) * 18) # tuned curve + return round(min(100, score), 1) + except Exception: + return None + + +@manual_predict.route("/predict_manual", methods=["POST"]) +def predict_manual(): + data = request.get_json(force=True, silent=True) or {} + + model_name = data.get("model") + values = data.get("values") # expecting a LIST (array) + if not model_name or not isinstance(values, list): + return jsonify({ + "error": "Expect JSON: { model: 'cicids'|'bcc', values: [v1, v2, ...] }" + }), 400 + + bundle = load_model(model_name) + model = bundle.get("model") + artifacts = bundle.get("artifacts") or {} + + if model is None: + return jsonify({"error": "Model not loaded"}), 500 + + try: + # Common metadata + model_info = { + "model_name": model_name, + "features": artifacts.get("features") or artifacts.get("feature_list") or bundle.get("features") or None, + "classes": None, + "train_counts": artifacts.get("train_counts") or artifacts.get("class_counts") or None, + "scaler_present": bool(artifacts.get("scaler")) or bool(bundle.get("scaler")), + } + + # helper to decode label_map / encoder (checks artifacts first, then bundle) + def decode_label(raw): + try: + # artifacts label_map (mapping value->name) + if artifacts.get("label_map"): + inv = {v: k for k, v in artifacts["label_map"].items()} + return inv.get(int(raw), str(raw)) + + # artifacts label_encoder + if artifacts.get("label_encoder"): + return artifacts["label_encoder"].inverse_transform([int(raw)])[0] + + # bundle-level encoder (e.g. realtime_encoder.pkl loaded in bundle) + if bundle.get("encoder"): + return bundle["encoder"].inverse_transform([int(raw)])[0] + except Exception as e: + # decoding failed; log and fallback to str + print("[decode_label] ERROR:", e) + # fallback: if raw already a string return it, else stringified raw + return str(raw) + + # CICIDS (13 features expected) + if model_name == "cicids": + feature_list = model_info["features"] + if not feature_list: + return jsonify({"error": "CICIDS artifacts missing 'features' list"}), 500 + + if len(values) != len(feature_list): + return jsonify({ + "error": f"CICIDS needs {len(feature_list)} features, received {len(values)}" + }), 400 + + X = np.array([[float(x) for x in values]], dtype=float) + + # apply scaler if present in artifacts or bundle + scaler = artifacts.get("scaler") or bundle.get("scaler") + scaled_row = None + try: + if scaler is not None: + scaled = scaler.transform(X) + scaled_row = np.array(scaled).tolist() + Xs = scaled + else: + Xs = X + except Exception as e: + # fallback to raw X if scaler fails + print("[predict_manual][CICIDS] scaler error:", e) + Xs = X + + # predict + pred_raw = model.predict(Xs)[0] + pred_label = decode_label(pred_raw) + + # probabilities + proba_max = None + probs = None + try: + if hasattr(model, "predict_proba"): + p = model.predict_proba(Xs)[0] + probs = [float(x) for x in p] + proba_max = float(max(p)) + except Exception: + pass + + # fill model_info.classes if possible (prefer encoder classes) + try: + if artifacts.get("label_encoder"): + model_info["classes"] = list(artifacts["label_encoder"].classes_) + elif bundle.get("encoder"): + model_info["classes"] = list(bundle["encoder"].classes_) + elif hasattr(model, "classes_"): + model_info["classes"] = [str(c) for c in model.classes_] + except Exception: + pass + + # compute reliability + train_counts = model_info.get("train_counts") + reliability = None + if train_counts and isinstance(train_counts, dict): + # try to get count for the predicted label (string keys) + reliability = _reliability_score_from_count( + train_counts.get(str(pred_label)) or train_counts.get(pred_raw) + ) + elif train_counts and isinstance(train_counts, list): + reliability = _reliability_score_from_count(sum(train_counts) / len(train_counts)) + else: + reliability = None + + resp = { + "prediction": pred_label, + "pred_raw": str(pred_raw), + "confidence": proba_max, + "proba_max": proba_max, + "probs": probs, + "raw_row": X.tolist()[0], + "scaled_row": scaled_row, + "model_info": model_info, + "reliability": reliability + } + return jsonify(resp) + + # BCC (15 features expected) + elif model_name == "bcc": + EXPECTED = 15 + if len(values) != EXPECTED: + return jsonify({ + "error": f"BCC needs {EXPECTED} features, received {len(values)}" + }), 400 + + X = np.array([[float(x) for x in values]], dtype=float) + + scaler = bundle.get("scaler") or artifacts.get("scaler") + scaled_row = None + try: + if scaler is not None: + scaled = scaler.transform(X) + scaled_row = np.array(scaled).tolist() + Xs = scaled + else: + Xs = X + except Exception as e: + print("[predict_manual][BCC] scaler error:", e) + Xs = X + + pred_raw = model.predict(Xs)[0] + pred_label = decode_label(pred_raw) + + proba_max = None + probs = None + try: + if hasattr(model, "predict_proba"): + p = model.predict_proba(Xs)[0] + probs = [float(x) for x in p] + proba_max = float(max(p)) + except Exception: + pass + + # model_info classes: prefer encoder classes if present + try: + encoder = bundle.get("encoder") or artifacts.get("label_encoder") + if encoder is not None: + model_info["classes"] = list(encoder.classes_) + elif hasattr(model, "classes_"): + # fallback - often these are numeric indices + model_info["classes"] = [str(c) for c in model.classes_] + except Exception: + pass + + train_counts = model_info.get("train_counts") + reliability = None + if train_counts and isinstance(train_counts, dict): + reliability = _reliability_score_from_count( + train_counts.get(str(pred_label)) or train_counts.get(pred_raw) + ) + elif train_counts: + reliability = _reliability_score_from_count(sum(train_counts) / len(train_counts)) + + resp = { + "prediction": pred_label, + "pred_raw": str(pred_raw), + "confidence": proba_max, + "proba_max": proba_max, + "probs": probs, + "raw_row": X.tolist()[0], + "scaled_row": scaled_row, + "model_info": model_info, + "reliability": reliability + } + return jsonify(resp) + + else: + return jsonify({"error": "unsupported model"}), 400 + + except Exception as e: + print("[predict_manual] Exception:", e) + return jsonify({"error": str(e)}), 500 + + +@manual_predict.route("/predict_debug", methods=["POST"]) +def predict_debug(): + """ + Debug endpoint: returns raw ordered vector, scaled vector (if scaler), + model classes, prediction, predict_proba (if available), and artifacts info. + Use this to compare what you *intend* to send vs what model receives. + """ + try: + data = request.get_json(force=True, silent=True) or {} + model_name = data.get("model") + feats = data.get("features") + if not model_name or not isinstance(feats, dict): + return jsonify({"error": "Provide JSON {model: 'cicids'|'bcc', features: {...}}"}), 400 + + bundle = load_model(model_name) + model = bundle.get("model") + artifacts = bundle.get("artifacts") or {} + + if model is None: + return jsonify({"error": "Model not loaded"}), 500 + + debug = {"model_name": model_name} + + if model_name == "cicids": + feature_list = artifacts.get("features") + debug["artifact_features"] = feature_list + # Build ordered row (float) + row = [float(feats.get(f, 0.0)) for f in (feature_list or [])] + debug["raw_row"] = row + + X = np.array([row], dtype=float) + + scaler = artifacts.get("scaler") or bundle.get("scaler") + if scaler is not None: + try: + Xs = scaler.transform(X) + debug["scaled_row"] = np.array(Xs).tolist() + except Exception as e: + debug["scaler_error"] = str(e) + Xs = X + else: + Xs = X + debug["scaled_row"] = None + + # predict + try: + pred_raw = model.predict(Xs)[0] + debug["pred_raw"] = repr(pred_raw) + # classes + try: + debug["model_classes"] = [str(c) for c in getattr(model, "classes_", [])] + except Exception: + debug["model_classes"] = None + # proba + if hasattr(model, "predict_proba"): + try: + probs = model.predict_proba(Xs)[0].tolist() + debug["probs"] = probs + debug["proba_max"] = max(probs) + except Exception as e: + debug["proba_error"] = str(e) + # decode label + label = str(pred_raw) + try: + if artifacts.get("label_map"): + inv = {v: k for k, v in artifacts["label_map"].items()} + label = inv.get(int(pred_raw), str(pred_raw)) + elif artifacts.get("label_encoder"): + label = artifacts["label_encoder"].inverse_transform([int(pred_raw)])[0] + elif bundle.get("encoder"): + label = bundle["encoder"].inverse_transform([int(pred_raw)])[0] + except Exception as e: + debug["label_decode_error"] = str(e) + + debug["label"] = label + except Exception as e: + debug["predict_error"] = str(e) + debug["predict_tb"] = traceback.format_exc() + + return jsonify(debug) + + elif model_name == "bcc": + # BCC: we will attempt to build 15-element row from expected keys or values + BCC_FEATURES = [ + "proto", "src_port", "dst_port", "flow_duration", "total_fwd_pkts", + "total_bwd_pkts", "flags_numeric", "payload_len", "header_len", + "rate", "iat", "syn", "ack", "rst", "fin" + ] + debug["expected_bcc_features"] = BCC_FEATURES + if all(k in feats for k in BCC_FEATURES): + row = [float(feats.get(k, 0.0)) for k in BCC_FEATURES] + else: + vals = list(feats.values()) + vals = [float(v) if (v is not None and str(v).strip() != "") else 0.0 for v in vals] + if len(vals) < 15: + vals = vals + [0.0] * (15 - len(vals)) + row = vals[:15] + debug["raw_row"] = row + X = np.array([row], dtype=float) + + # try scaler from bundle or artifacts + scaler = bundle.get("scaler") or artifacts.get("scaler") + if scaler is not None: + try: + Xs = scaler.transform(X) + debug["scaled_row"] = np.array(Xs).tolist() + except Exception as e: + debug["scaler_error"] = str(e) + Xs = X + else: + Xs = X + debug["scaled_row"] = None + + try: + pred_raw = model.predict(Xs)[0] + debug["pred_raw"] = repr(pred_raw) + # model raw classes (may be numeric) + debug["model_classes"] = [str(c) for c in getattr(model, "classes_", [])] + if hasattr(model, "predict_proba"): + try: + probs = model.predict_proba(Xs)[0].tolist() + debug["probs"] = probs + debug["proba_max"] = max(probs) + except Exception as e: + debug["proba_error"] = str(e) + # decode using encoder if present (bundle or artifacts) + label = str(pred_raw) + try: + encoder = bundle.get("encoder") or artifacts.get("label_encoder") + if encoder: + label = encoder.inverse_transform([int(pred_raw)])[0] + except Exception as e: + debug["label_decode_error"] = str(e) + debug["label"] = label + except Exception as e: + debug["predict_error"] = str(e) + debug["predict_tb"] = traceback.format_exc() + + return jsonify(debug) + + else: + return jsonify({"error": "unsupported model"}), 400 + + except Exception as e: + return jsonify({"error": str(e), "tb": traceback.format_exc()}), 500 + + +@manual_predict.route("/retrain_request", methods=["POST"]) +def retrain_request(): + data = request.get_json() or {} + # Save retrain request to a file for later processing + with open("retrain_requests.jsonl", "a") as f: + f.write(json.dumps(data) + "\n") + + return jsonify({"status": "saved", "msg": "Retrain request recorded"}) diff --git a/backend/routes/ml_route.py b/backend/routes/ml_route.py new file mode 100644 index 0000000000000000000000000000000000000000..622f25ab07da72c325e1cd6cda04cb0c672a094e --- /dev/null +++ b/backend/routes/ml_route.py @@ -0,0 +1,297 @@ +# ========================================================== +# backend/routes/ml_route.py +# Adaptive AI Framework - ML Route for NIDS Intelligence +# ========================================================== + +from flask import Blueprint, request, jsonify +import threading +import time +import os +import joblib +import random +from datetime import datetime +from flask_cors import cross_origin +import numpy as np + +ml_bp = Blueprint("ml_bp", __name__) + +ML_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "ml_models")) + +# In-memory global stores +MODELS = {} +RETRAIN_STATUS = {"running": False, "progress": 0, "message": "", "last_result": None} +METRICS_CACHE = {} + +# Your NIDS feature list (15) +FEATURE_NAMES = [ + "protocol", "src_port", "dst_port", "duration", "packets_count", + "fwd_packets_count", "bwd_packets_count", "total_payload_bytes", + "total_header_bytes", "bytes_rate", "packets_rate", + "syn_flag_counts", "ack_flag_counts", "rst_flag_counts", "fin_flag_counts" +] + +# ========================================================== +# 🧠 Model Management +# ========================================================== + +def try_load_models(): + """Load models from disk (if available).""" + global MODELS + MODELS = {} + try: + files = os.listdir(ML_DIR) + except Exception: + files = [] + + for fname in files: + if fname.endswith(".pkl"): + name = os.path.splitext(fname)[0] + try: + m = joblib.load(os.path.join(ML_DIR, fname)) + MODELS[name] = {"obj": m, "name": name, "path": os.path.join(ML_DIR, fname)} + except Exception as e: + MODELS[name] = {"obj": None, "name": name, "path": os.path.join(ML_DIR, fname), "load_error": str(e)} + +try_load_models() + +def model_summary(name, entry): + obj = entry.get("obj") + info = { + "id": name, + "name": name, + "type": type(obj).__name__ if obj is not None else "Unknown", + "accuracy": None, + "f1_score": None, + "dataset": "unknown", + "status": "Active" if obj is not None else "Unavailable", + "last_trained": None, + } + meta = getattr(obj, "metadata", None) + if isinstance(meta, dict): + info.update({ + "accuracy": meta.get("accuracy"), + "f1_score": meta.get("f1_score"), + "dataset": meta.get("dataset"), + "last_trained": meta.get("last_trained"), + }) + return info + +# ========================================================== +# 📦 ROUTES +# ========================================================== + +@ml_bp.route("/ml/models", methods=["GET"]) +def list_models(): + """Return all available models.""" + try_load_models() + out = [model_summary(name, entry) for name, entry in MODELS.items()] + + if not out: + out = [{ + "id": "placeholder_model", + "name": "Placeholder Detector", + "type": "Simulated", + "accuracy": 92.1, + "f1_score": 0.90, + "dataset": "Simulated-NIDS", + "status": "Active", + "last_trained": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + }] + return jsonify(out) + + +@ml_bp.route("/ml/metrics", methods=["GET"]) +def get_metrics(): + """Return metrics like accuracy history & class distribution.""" + if METRICS_CACHE: + return jsonify(METRICS_CACHE) + + accuracy_history = [ + {"epoch": i + 1, "accuracy": round(0.8 + i * 0.04 + random.random() * 0.01, 3)} + for i in range(10) + ] + class_distribution = { + "Normal": 1500, + "DDoS": 420, + "PortScan": 260, + "Botnet": 140, + "VPN": 120, + "TOR": 90 + } + METRICS_CACHE.update({ + "accuracy_history": accuracy_history, + "class_distribution": class_distribution + }) + return jsonify(METRICS_CACHE) + +# ========================================================== +# 🔮 PREDICTION ENDPOINT +# ========================================================== + +def safe_predict_with_model(model_entry, features): + """Try to predict with a loaded model.""" + obj = model_entry.get("obj") + if obj is None: + return None + try: + if isinstance(features, dict): + features_list = [features.get(k, 0) for k in FEATURE_NAMES] + else: + features_list = features + X = [features_list] + + if hasattr(obj, "predict_proba"): + probs = obj.predict_proba(X)[0] + pred_idx = int(np.argmax(probs)) + pred_label = obj.classes_[pred_idx] if hasattr(obj, "classes_") else str(pred_idx) + return {"prediction": str(pred_label), "confidence": float(round(probs[pred_idx], 4))} + else: + pred = obj.predict(X)[0] + return {"prediction": str(pred), "confidence": 1.0} + except Exception: + return None + + +@ml_bp.route("/ml/predict-test", methods=["POST"]) +def predict_test(): + """Accept feature dict and return prediction result.""" + data = request.get_json() or {} + features = data.get("features") or data.get("sample") + model_name = data.get("model") + + try: + if not features: + return jsonify({"error": "No features provided"}), 400 + + try_load_models() + chosen = None + if model_name and model_name in MODELS: + chosen = MODELS[model_name] + else: + for k, e in MODELS.items(): + if e.get("obj") is not None: + chosen = e + break + + if chosen: + res = safe_predict_with_model(chosen, features) + if res: + res.update({"model_used": chosen.get("name")}) + return jsonify(res) + + # Fallback simulated prediction + classes = ["Normal", "DDoS", "PortScan", "Botnet", "VPN", "TOR"] + pred = random.choice(classes) + confidence = round(random.uniform(0.7, 0.99), 3) + return jsonify({ + "prediction": pred, + "confidence": confidence, + "model_used": "SimulatedDetector", + "used_features": FEATURE_NAMES + }) + + except Exception as e: + return jsonify({"error": str(e)}), 500 + +# ========================================================== +# ⚙️ RETRAIN SIMULATION +# ========================================================== + +def _retrain_job(model_id=None, epochs=6): + RETRAIN_STATUS["running"] = True + RETRAIN_STATUS["progress"] = 0 + RETRAIN_STATUS["message"] = "Starting retrain..." + best_acc = 0.0 + try: + for e in range(1, epochs + 1): + RETRAIN_STATUS["message"] = f"Epoch {e}/{epochs}..." + for p in range(5): + time.sleep(0.45) + RETRAIN_STATUS["progress"] = int(((e - 1) * 100 / epochs) + (p + 1) * (100 / (epochs * 5))) + best_acc = round(0.85 + (e * 0.02) + random.random() * 0.01, 4) + RETRAIN_STATUS["message"] = f"Epoch {e} finished. acc: {best_acc}" + RETRAIN_STATUS["message"] = "Finalizing..." + time.sleep(0.6) + RETRAIN_STATUS["last_result"] = { + "accuracy": best_acc, + "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S") + } + METRICS_CACHE.setdefault("accuracy_history", []).append({ + "epoch": len(METRICS_CACHE.get("accuracy_history", [])) + 1, + "accuracy": best_acc + }) + except Exception as e: + RETRAIN_STATUS["message"] = f"Error: {e}" + finally: + RETRAIN_STATUS["running"] = False + RETRAIN_STATUS["progress"] = 100 + + +@ml_bp.route("/ml/retrain", methods=["POST"]) +def retrain(): + """Start retraining in background thread.""" + if RETRAIN_STATUS.get("running"): + return jsonify({"error": "Retrain already in progress"}), 409 + payload = request.get_json() or {} + model_id = payload.get("model") + epochs = int(payload.get("epochs", 6)) + t = threading.Thread(target=_retrain_job, args=(model_id, epochs), daemon=True) + t.start() + return jsonify({"message": "Retrain started", "epochs": epochs}) + + +@ml_bp.route("/ml/retrain/status", methods=["GET"]) +def retrain_status(): + """Get retrain progress.""" + return jsonify(RETRAIN_STATUS) + +# ========================================================== +# 🧩 FEATURE IMPORTANCE +# ========================================================== + +@ml_bp.route("/feature-importance/", methods=["GET"]) +@cross_origin() +def feature_importance(model_id): + """Return actual or simulated feature importances.""" + try: + try_load_models() + entry = MODELS.get(model_id) + mdl = entry.get("obj") if entry else None + + fi = [] + if mdl is not None and hasattr(mdl, "feature_importances_"): + arr = np.array(getattr(mdl, "feature_importances_")).flatten() + arr = arr[:len(FEATURE_NAMES)] + for i, v in enumerate(arr): + fi.append({"feature": FEATURE_NAMES[i], "importance": float(v)}) + elif mdl is not None and hasattr(mdl, "coef_"): + arr = np.abs(np.array(getattr(mdl, "coef_")).flatten()) + arr = arr[:len(FEATURE_NAMES)] + total = float(np.sum(arr)) or 1.0 + for i, v in enumerate(arr): + fi.append({"feature": FEATURE_NAMES[i], "importance": float(v / total * 100.0)}) + else: + simulated = { + "protocol": 8.2, + "src_port": 7.1, + "dst_port": 6.4, + "duration": 10.5, + "packets_count": 7.8, + "fwd_packets_count": 6.9, + "bwd_packets_count": 6.5, + "total_payload_bytes": 9.8, + "total_header_bytes": 8.6, + "bytes_rate": 9.9, + "packets_rate": 9.1, + "syn_flag_counts": 5.3, + "ack_flag_counts": 4.9, + "rst_flag_counts": 3.8, + "fin_flag_counts": 3.2 + } + fi = [{"feature": f, "importance": float(v)} for f, v in simulated.items()] + + return jsonify({"model_id": model_id, "feature_importance": fi}) + + except Exception as e: + return jsonify({"error": str(e)}), 500 + diff --git a/backend/routes/ml_switch_route.py b/backend/routes/ml_switch_route.py new file mode 100644 index 0000000000000000000000000000000000000000..cd494efc26bd5d025c901ef429024b1abe188147 --- /dev/null +++ b/backend/routes/ml_switch_route.py @@ -0,0 +1,105 @@ +# backend/routes/ml_switch_route.py +from flask import Blueprint, request, jsonify +from utils.model_selector import set_active_model, get_active_model, load_model + +ml_switch = Blueprint("ml_switch", __name__) + +@ml_switch.route("/active", methods=["GET"]) +def active(): + return jsonify({"active_model": get_active_model()}) + +@ml_switch.route("/select", methods=["POST"]) +def select(): + data = request.get_json(force=True, silent=True) or {} + model = data.get("model") + if model not in ("bcc", "cicids"): + return jsonify({"error": "model must be 'bcc' or 'cicids'"}), 400 + try: + set_active_model(model) + # attempt load to give quick feedback + info = load_model(model) + return jsonify({"message": f"Active model set to {model}", "loaded": bool(info)}) + + except Exception as e: + return jsonify({"error": str(e)}), 500 + + +@ml_switch.route("/health", methods=["GET"]) +def health(): + import numpy as np + import pandas as pd + + active = get_active_model() + bundle = load_model(active) + + model = bundle.get("model") + artifacts = bundle.get("artifacts") + + # Default responses + artifact_keys = list(artifacts.keys()) if artifacts else [] + features = None + feature_count = 0 + test_prediction = "N/A" + + # ------------------------------------------ + # CICIDS HEALTH CHECK + # ------------------------------------------ + if active == "cicids": + if artifacts and "features" in artifacts: + features = artifacts["features"] + feature_count = len(features) + + try: + # generate a zero vector + X = np.zeros((1, feature_count)) + scaler = artifacts.get("scaler") + if scaler: + X = scaler.transform(X) + + pred = model.predict(X)[0] + test_prediction = str(pred) + + except Exception as e: + test_prediction = f"Error: {str(e)}" + + # ------------------------------------------ + # BCC HEALTH CHECK + # ------------------------------------------ + elif active == "bcc": + try: + # Create minimal fake BCC packet feature vector: 15 values + X = np.zeros((1, 15)) + + scaler = bundle.get("scaler") + encoder = bundle.get("encoder") + + if scaler: + Xs = scaler.transform(X) + else: + Xs = X + + pred_raw = model.predict(Xs)[0] + + if encoder: + pred = encoder.inverse_transform([int(pred_raw)])[0] + else: + pred = str(pred_raw) + + test_prediction = f"OK: {pred}" + + except Exception as e: + test_prediction = f"Error: {str(e)}" + + # ------------------------------------------ + # Build response + # ------------------------------------------ + return { + "active_model": active, + "model_loaded": model is not None, + "artifact_keys": artifact_keys, + "feature_count": feature_count, + "features": features, + "test_prediction": test_prediction + } + + diff --git a/backend/routes/offline_detection.py b/backend/routes/offline_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..9e7b66a8643ba4063357c95c424d5a1aa9ea210b --- /dev/null +++ b/backend/routes/offline_detection.py @@ -0,0 +1,139 @@ +import os +import pandas as pd +from flask import Blueprint, request, jsonify, send_file +from werkzeug.utils import secure_filename +from datetime import datetime +import joblib +from fpdf import FPDF +from utils.pcap_to_csv import convert_pcap_to_csv + +offline_bp = Blueprint("offline_bp", __name__) + +UPLOAD_DIR = "uploads" +SAMPLE_DIR = "sample" +os.makedirs(UPLOAD_DIR, exist_ok=True) +os.makedirs(SAMPLE_DIR, exist_ok=True) + +ALLOWED_EXT = {"csv", "pcap"} + +# Features +BCC_FEATURES = [ + "proto","src_port","dst_port","flow_duration","total_fwd_pkts","total_bwd_pkts", + "flags_numeric","payload_len","header_len","rate","iat","syn","ack","rst","fin" +] + +CICIDS_FEATURES = [ + "Protocol","Dst Port","Flow Duration","Tot Fwd Pkts","Tot Bwd Pkts", + "TotLen Fwd Pkts","TotLen Bwd Pkts","Fwd Pkt Len Mean","Bwd Pkt Len Mean", + "Flow IAT Mean","Fwd PSH Flags","Fwd URG Flags","Fwd IAT Mean" +] + +# Models +bcc_model = joblib.load("ml_models/realtime_model.pkl") +bcc_encoder = joblib.load("ml_models/realtime_encoder.pkl") +bcc_scaler = joblib.load("ml_models/realtime_scaler.pkl") + +cicids_model = joblib.load("ml_models/rf_pipeline.joblib") + + +def allowed(filename): + return "." in filename and filename.rsplit(".", 1)[1].lower() in ALLOWED_EXT + + +# 📌 Sample CSV Download +@offline_bp.route("/sample/", methods=["GET"]) +def download_sample(model): + file_path = None + if model == "bcc": + file_path = os.path.join(SAMPLE_DIR, "bcc_sample.csv") + elif model == "cicids": + file_path = os.path.join(SAMPLE_DIR, "cicids_sample.csv") + else: + return jsonify(success=False, message="Invalid model"), 400 + + if not os.path.exists(file_path): + return jsonify(success=False, message="Sample file missing"), 404 + + return send_file(file_path, as_attachment=True) + + +# 📌 Prediction API +@offline_bp.route("/predict", methods=["POST"]) +def offline_predict(): + if "file" not in request.files: + return jsonify(success=False, message="No file uploaded"), 400 + + file = request.files["file"] + model_type = request.form.get("model", "bcc") + + if not allowed(file.filename): + return jsonify(success=False, message="Unsupported file type"), 400 + + filename = secure_filename(file.filename) + saved_path = os.path.join(UPLOAD_DIR, filename) + file.save(saved_path) + + # PCAP Conversion + if filename.lower().endswith(".pcap"): + saved_path = convert_pcap_to_csv(saved_path) + + + df = pd.read_csv(saved_path) + # Prevent empty CSV prediction + if df.shape[0] == 0: + return jsonify(success=False, message="CSV has no data rows to analyze!"), 400 + + expected = BCC_FEATURES if model_type == "bcc" else CICIDS_FEATURES + + missing = [c for c in expected if c not in df.columns] + if missing: + return jsonify(success=False, message=f"Missing features: {missing}") + + df = df[expected] + + if model_type == "bcc": + scaled = bcc_scaler.transform(df) + preds = bcc_model.predict(scaled) + labels = bcc_encoder.inverse_transform(preds) + else: + labels = cicids_model.predict(df) + + df["prediction"] = labels + class_counts = df["prediction"].value_counts().to_dict() + + results = [{"index": i, "class": lbl} for i, lbl in enumerate(labels)] + + result_file = os.path.join(UPLOAD_DIR, "last_results.csv") + df.to_csv(result_file, index=False) + + return jsonify(success=True, classCounts=class_counts, results=results) + + +# 📌 PDF Report Generation +@offline_bp.route("/report", methods=["GET"]) +def offline_report(): + result_file = os.path.join(UPLOAD_DIR, "last_results.csv") + if not os.path.exists(result_file): + return jsonify(success=False, message="Run prediction first"), 400 + + df = pd.read_csv(result_file) + class_counts = df["prediction"].value_counts().to_dict() + + pdf_path = os.path.join(UPLOAD_DIR, "offline_report.pdf") + + pdf = FPDF() + pdf.add_page() + pdf.set_font("Arial", "B", 16) + pdf.cell(0, 10, "AI-NIDS Offline Threat Analysis Report", ln=True) + + pdf.set_font("Arial", size=12) + pdf.cell(0, 10, f"Generated: {datetime.now()}", ln=True) + pdf.ln(5) + + for c, v in class_counts.items(): + pdf.cell(0, 8, f"{c}: {v}", ln=True) + + pdf.output(pdf_path) + return send_file(pdf_path, as_attachment=True) + + diff --git a/backend/routes/predict_route.py b/backend/routes/predict_route.py new file mode 100644 index 0000000000000000000000000000000000000000..529b7c73fdd9f71142b6b5b91664c65195be066a --- /dev/null +++ b/backend/routes/predict_route.py @@ -0,0 +1,132 @@ +# backend/routes/predict_route.py +from flask import Blueprint, request, jsonify +import time +import numpy as np +import pandas as pd +from utils.model_selector import load_model, get_active_model +from utils.logger import classify_risk + +predict_bp = Blueprint("predict", __name__) + +@predict_bp.route("/", methods=["GET"]) +def info(): + active = get_active_model() + return jsonify({ + "message": "POST JSON to /api/predict/ to get model prediction.", + "active_model": active, + "note": "For 'bcc' model send ordered features or dict; for 'cicids' send named features matching artifacts['features']." + }) + +@predict_bp.route("/", methods=["POST"]) +def predict(): + active = get_active_model() + mdl = load_model(active) + + if active == "bcc": + model = mdl.get("model") + scaler = mdl.get("scaler") + encoder = mdl.get("encoder") + + if model is None or scaler is None or encoder is None: + return jsonify({"error": "BCC model/scaler/encoder not loaded on server."}), 500 + + data = request.get_json(force=True, silent=True) + if data is None: + return jsonify({"error": "No JSON body provided"}), 400 + + # Accept either list/array or dict of features + # You must keep the same feature order as used in training (15 values) + if isinstance(data, dict): + # if the client provides named keys, try to coerce to ordered list + # fallback: take values in insertion order + vals = list(data.values()) + else: + vals = list(data) + + try: + X = np.array([float(v) for v in vals], dtype=float).reshape(1, -1) + except Exception as e: + return jsonify({"error": f"Failed to coerce input to numeric vector: {e}"}), 400 + + try: + Xs = scaler.transform(X) + except Exception: + # fallback: try prediction without scaler + Xs = X + + try: + pred_idx = model.predict(Xs)[0] + conf = None + if hasattr(model, "predict_proba"): + conf = float(np.max(model.predict_proba(Xs))) * 100.0 + label = encoder.inverse_transform([int(pred_idx)])[0] + risk = classify_risk(label) + return jsonify({ + "prediction": str(label), + "confidence": round(conf, 2) if conf is not None else None, + "risk_level": risk + }) + except Exception as e: + return jsonify({"error": f"Model predict failed: {str(e)}"}), 500 + + elif active == "cicids": + obj = mdl.get("artifacts", None) + model = mdl.get("model", None) + if model is None or obj is None: + return jsonify({"error": "CICIDS model or artifacts not available on server."}), 500 + + # artifacts expected to have 'features' and 'scaler' + features = obj.get("features") or obj.get("features_used") or obj.get("feature_list") + scaler = obj.get("scaler") or obj.get("scaler_object") + + if not features or scaler is None: + return jsonify({"error": "CICIDS artifacts missing features or scaler."}), 500 + + data = request.get_json(force=True, silent=True) + if data is None: + return jsonify({"error": "No JSON body provided"}), 400 + + # Accept dict of named features or list + if isinstance(data, dict): + # build row using artifacts feature order (missing -> 0) + row = [float(data.get(f, 0)) for f in features] + else: + # list or array + try: + row = [float(x) for x in data] + except Exception as e: + return jsonify({"error": "Provided input must be array or dict of numbers."}), 400 + + if len(row) != len(features): + return jsonify({"error": f"Expecting {len(features)} features for cicids: {features}"}), 400 + + X_df = pd.DataFrame([row], columns=features) + try: + Xs = scaler.transform(X_df) + except Exception: + Xs = X_df.values + + try: + pred = model.predict(Xs)[0] + conf = None + if hasattr(model, "predict_proba"): + conf = float(np.max(model.predict_proba(Xs))) * 100.0 + # label may already be string; try safe conversion + try: + label = str(pred) + except Exception: + label = repr(pred) + + risk = classify_risk(label) + return jsonify({ + "prediction": label, + "confidence": round(conf, 2) if conf else None, + "risk_level": risk + }) + except Exception as e: + return jsonify({"error": f"CICIDS predict failed: {str(e)}"}), 500 + + else: + return jsonify({"error": "Unknown active model"}), 500 + + diff --git a/backend/routes/reports_route.py b/backend/routes/reports_route.py new file mode 100644 index 0000000000000000000000000000000000000000..45b57bcccc281a3fdbffc8d31c1d276e2aee7447 --- /dev/null +++ b/backend/routes/reports_route.py @@ -0,0 +1,172 @@ +# backend/routes/reports_route.py +from flask import Blueprint, jsonify, request, send_file +from fpdf import FPDF +from io import BytesIO +from datetime import datetime, timedelta +from flask_mail import Message +from extensions import mail +import random + +reports_bp = Blueprint("reports_bp", __name__) + +# Fake (but structured) historical attack data +CLASSES = ["DDoS", "VPN", "TOR", "I2P", "SQL Injection", "Malware"] + +def generate_fake_data(days=7): + today = datetime.now() + data = [] + for i in range(days): + date = (today - timedelta(days=i)).strftime("%Y-%m-%d") + day_data = {cls: random.randint(20, 200) for cls in CLASSES} + day_data["date"] = date + data.append(day_data) + return list(reversed(data)) + +ATTACK_DATA = generate_fake_data(14) + +# -------------------------------------------------------- +@reports_bp.route("/", methods=["GET"]) +def reports_overview(): + total_attacks = sum(sum(v for k, v in day.items() if k != "date") for day in ATTACK_DATA) + recent = ATTACK_DATA[-1] + return jsonify({ + "total_attacks": total_attacks, + "last_day": recent["date"], + "last_day_total": sum(v for k, v in recent.items() if k != "date"), + }) + +# -------------------------------------------------------- +@reports_bp.route("/trend", methods=["GET"]) +def attack_trend(): + trend = [{"date": d["date"], "attacks": sum(v for k, v in d.items() if k != "date")} for d in ATTACK_DATA] + return jsonify(trend) + +# -------------------------------------------------------- +@reports_bp.route("/distribution", methods=["GET"]) +def attack_distribution(): + total = {cls: sum(day.get(cls, 0) for day in ATTACK_DATA) for cls in CLASSES} + return jsonify(total) + +# -------------------------------------------------------- +@reports_bp.route("/generate", methods=["GET"]) +def generate_report_pdf(): + pdf = FPDF() + pdf.add_page() + pdf.set_font("Helvetica", "B", 16) + pdf.cell(0, 10, "Adaptive AI NIDS - Attack Report", ln=True, align="C") + + pdf.set_font("Helvetica", "", 12) + pdf.ln(8) + pdf.cell(0, 10, f"Generated on {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", ln=True) + pdf.ln(6) + + pdf.set_font("Helvetica", "B", 13) + pdf.cell(0, 10, "Summary:", ln=True) + pdf.set_font("Helvetica", "", 11) + total = {cls: sum(day.get(cls, 0) for day in ATTACK_DATA) for cls in CLASSES} + for cls, val in total.items(): + pdf.cell(0, 8, f" • {cls}: {val} attacks", ln=True) + + pdf.ln(8) + pdf.set_font("Helvetica", "I", 10) + pdf.multi_cell(0, 8, + "This report summarizes attack activity captured by the Adaptive AI NIDS system. " + "It includes class-wise distribution and historical trend for the past two weeks.") + + # Output to memory + buffer = BytesIO() + pdf.output(buffer) + buffer.seek(0) + filename = f"NIDS_Report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf" + return send_file(buffer, as_attachment=True, download_name=filename, mimetype="application/pdf") + +# -------------------------------------------------------- +@reports_bp.route("/email", methods=["POST"]) +def send_report_email(): + data = request.get_json() + recipient = data.get("email") + if not recipient: + return jsonify({"error": "No recipient email provided"}), 400 + + # Generate detailed PDF + pdf_buffer = BytesIO() + pdf = FPDF() + pdf.add_page() + pdf.set_font("Helvetica", "B", 16) + pdf.cell(0, 10, "Adaptive AI NIDS - Full System Report", ln=True, align="C") + pdf.ln(8) + pdf.set_font("Helvetica", "", 12) + pdf.cell(0, 10, "Summary of recent network activity:", ln=True) + pdf.ln(5) + + pdf.set_font("Helvetica", "B", 12) + pdf.cell(0, 8, "Attack Distribution:", ln=True) + total = {cls: sum(day.get(cls, 0) for day in ATTACK_DATA) for cls in CLASSES} + pdf.set_font("Helvetica", "", 11) + for cls, val in total.items(): + pdf.cell(0, 8, f" - {cls}: {val} attacks", ln=True) + + pdf.ln(6) + pdf.set_font("Helvetica", "B", 12) + pdf.cell(0, 8, "Recent Trend (last 7 days):", ln=True) + pdf.set_font("Helvetica", "", 11) + for d in ATTACK_DATA[-7:]: + pdf.cell(0, 8, f"{d['date']}: {sum(v for k, v in d.items() if k != 'date')} total", ln=True) + + pdf.ln(10) + pdf.set_font("Helvetica", "I", 10) + pdf.multi_cell(0, 8, "This automated report is generated by Adaptive AI NIDS. " + "It summarizes live detections, system diagnostics, and " + "AI-identified attack classes.") + + pdf.output(pdf_buffer) + pdf_buffer.seek(0) + + try: + msg = Message( + subject="Adaptive AI NIDS - Full Report", + recipients=[recipient], + body="Attached is your Adaptive AI NIDS summary report with recent attack data.", + ) + msg.attach("Adaptive_NIDS_Report.pdf", "application/pdf", pdf_buffer.read()) + mail.send(msg) + return jsonify({"success": True, "message": f"Email sent to {recipient}"}) + except Exception as e: + return jsonify({"error": str(e)}), 500 + + + +@reports_bp.route("/list", methods=["GET"]) +def list_reports(): + reports = [ + { + "id": 1, + "name": "System Health Summary", + "type": "System Health", + "size": "420 KB", + "date": datetime.now().strftime("%Y-%m-%d %H:%M"), + "endpoint": "/api/reports/generate" + }, + { + "id": 2, + "name": "Network Attack Analysis", + "type": "Network Analysis", + "size": "1.2 MB", + "date": datetime.now().strftime("%Y-%m-%d %H:%M"), + "endpoint": "/api/reports/generate" + }, + { + "id": 3, + "name": "Threat Intelligence Summary", + "type": "Threat Intelligence", + "size": "620 KB", + "date": datetime.now().strftime("%Y-%m-%d %H:%M"), + "endpoint": "/api/reports/generate" + }, + ] + return jsonify(reports) + + + + + diff --git a/backend/routes/system_info.py b/backend/routes/system_info.py new file mode 100644 index 0000000000000000000000000000000000000000..b4dea609c31da93470d715e199c0b3a83a420427 --- /dev/null +++ b/backend/routes/system_info.py @@ -0,0 +1,211 @@ +from flask import Blueprint, jsonify +import psutil +import platform +import socket +from datetime import datetime +import random +import time +import random +import io +from fpdf import FPDF +from flask import send_file + + + + +system_bp = Blueprint("system", __name__) + +@system_bp.route("/system/status", methods=["GET"]) +def system_status(): + try: + hostname = socket.gethostname() + ip_address = socket.gethostbyname(hostname) + os_info = platform.platform() + cpu_name = platform.processor() + + # --- Metrics --- + cpu_percent = psutil.cpu_percent(interval=0.5) + ram = psutil.virtual_memory() + disk = psutil.disk_usage('/') + net_io = psutil.net_io_counters() + + # --- Temperature --- + try: + temps = psutil.sensors_temperatures() + cpu_temp = ( + temps.get("coretemp")[0].current + if "coretemp" in temps + else random.uniform(45.0, 75.0) # fallback + ) + except Exception: + cpu_temp = random.uniform(45.0, 75.0) + + # --- AI Health Score --- + # Weighted average (higher = better) + usage = (cpu_percent * 0.4 + ram.percent * 0.3 + disk.percent * 0.3) + health_score = max(0, 100 - usage) + + data = { + "hostname": hostname, + "ip_address": ip_address, + "os": os_info, + "cpu_name": cpu_name, + "cpu_usage": round(cpu_percent, 2), + "ram_usage": round(ram.percent, 2), + "disk_usage": round(disk.percent, 2), + "ram_total": round(ram.total / (1024 ** 3), 2), + "disk_total": round(disk.total / (1024 ** 3), 2), + "network_sent": round(net_io.bytes_sent / (1024 ** 2), 2), + "network_recv": round(net_io.bytes_recv / (1024 ** 2), 2), + "cpu_temp": round(cpu_temp, 2), + "health_score": round(health_score, 2), + "timestamp": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + } + return jsonify(data) + except Exception as e: + return jsonify({"error": str(e)}), 500 + +@system_bp.route("/system/diagnostic", methods=["GET"]) +def run_diagnostic(): + """Simulate a full AI-powered system stability diagnostic.""" + try: + # Simulated stress test (CPU, memory response) + cpu_load = random.uniform(60, 98) + ram_stress = random.uniform(50, 95) + disk_io = random.uniform(40, 90) + latency = random.uniform(15, 100) + + # AI stability score (100 = perfect) + stability = 100 - ((cpu_load * 0.3) + (ram_stress * 0.3) + (disk_io * 0.2) + (latency * 0.2)) / 2 + stability = round(max(0, min(100, stability)), 2) + + # Fake attack summary data + attacks = { + "total_attacks": random.randint(1200, 4200), + "blocked": random.randint(1100, 4000), + "missed": random.randint(5, 20), + "recent_threats": [ + {"type": "DDoS Flood", "risk": "High", "ip": "45.77.23.9"}, + {"type": "SQL Injection", "risk": "Medium", "ip": "103.54.66.120"}, + {"type": "VPN Evasion", "risk": "Low", "ip": "198.168.12.45"}, + ], + } + + diagnostic = { + "cpu_load": round(cpu_load, 2), + "ram_stress": round(ram_stress, 2), + "disk_io": round(disk_io, 2), + "latency": round(latency, 2), + "stability_score": stability, + "attacks": attacks, + "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"), + } + return jsonify(diagnostic) + except Exception as e: + return jsonify({"error": str(e)}), 500 + + + +@system_bp.route("/system/report", methods=["GET"]) +def generate_system_report(): + """Generate a downloadable PDF system report.""" + try: + # --- Simulated data or pull from live sources --- + system_status = { + "OS": "Windows 10 Pro", + "CPU": "Intel i5-12700H", + "Memory": "16 GB", + "Disk": "512 GB SSD", + "IP": "127.0.0.1", + "Health Score": "89%", + "Last Diagnostic": datetime.now().strftime("%Y-%m-%d %H:%M:%S"), + } + + # --- Create PDF report --- + pdf = FPDF() + pdf.add_page() + pdf.set_auto_page_break(auto=True, margin=15) + + # Title + pdf.set_font("Helvetica", "B", 18) + pdf.cell(0, 10, "Adaptive AI NIDS - System Report", ln=True, align="C") + pdf.ln(10) + + # Subtitle + pdf.set_font("Helvetica", "", 12) + pdf.cell(0, 10, f"Generated on: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", ln=True) + pdf.ln(8) + + # Section: System Status + pdf.set_font("Helvetica", "B", 14) + pdf.cell(0, 10, "System Information", ln=True) + pdf.set_font("Helvetica", "", 12) + pdf.ln(5) + + for key, value in system_status.items(): + pdf.cell(0, 8, f"{key}: {value}", ln=True) + + pdf.ln(10) + pdf.set_font("Helvetica", "B", 14) + pdf.cell(0, 10, "Attack Summary (Last 24h)", ln=True) + pdf.set_font("Helvetica", "", 12) + pdf.ln(5) + + pdf.cell(0, 8, "Total Attacks Detected: 3471", ln=True) + pdf.cell(0, 8, "High Risk: 512", ln=True) + pdf.cell(0, 8, "Medium Risk: 948", ln=True) + pdf.cell(0, 8, "Low Risk: 2011", ln=True) + + pdf.ln(10) + pdf.set_font("Helvetica", "I", 10) + pdf.cell(0, 8, "This report is automatically generated by Adaptive AI NIDS.", ln=True, align="C") + + # Save to memory + buffer = io.BytesIO() + pdf.output(buffer) + buffer.seek(0) + + filename = f"System_Report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pdf" + return send_file(buffer, as_attachment=True, download_name=filename, mimetype="application/pdf") + + except Exception as e: + return jsonify({"error": str(e)}), 500 + + +@system_bp.route("/system/processes") +def system_processes(): + try: + processes = [] + for proc in psutil.process_iter(['name', 'cpu_percent', 'memory_percent', 'status']): + info = proc.info + processes.append({ + "name": info.get("name", "Unknown"), + "cpu": round(info.get("cpu_percent", 0), 2), + "mem": round(info.get("memory_percent", 0), 2), + "status": info.get("status", "N/A"), + }) + # ✅ Sort by CPU usage and keep top 6 + top_processes = sorted(processes, key=lambda p: p["cpu"], reverse=True)[:6] + return jsonify(top_processes) + except Exception as e: + return jsonify({"error": str(e)}), 500 + + + +@system_bp.route("/system/connections") +def system_connections(): + try: + conns = [] + for c in psutil.net_connections(kind='inet'): + if c.laddr: + conns.append({ + "ip": c.laddr.ip, + "port": c.laddr.port, + "proto": "TCP" if c.type == socket.SOCK_STREAM else "UDP", + "state": c.status, + }) + # ✅ Only top 6 most recent/active connections + top_conns = conns[:6] + return jsonify(top_conns) + except Exception as e: + return jsonify({"error": str(e)}), 500 diff --git a/backend/routes/traffic_routes.py b/backend/routes/traffic_routes.py new file mode 100644 index 0000000000000000000000000000000000000000..e8424b56506988a213a2287cf8e9a8cb2db3e750 --- /dev/null +++ b/backend/routes/traffic_routes.py @@ -0,0 +1,49 @@ +# traffic_routes.py +from flask import Blueprint, jsonify +from utils.logger import get_recent_events, summarize_counts +from flow_builder import build_flows + +traffic_bp = Blueprint("traffic_bp", __name__) + +@traffic_bp.route("traffic/flows") +def flows(): + """Return aggregated flows from recent network events.""" + events = get_recent_events(2000) + flows = build_flows(events) + return jsonify({"flows": flows}) + + +@traffic_bp.route("traffic/protocols") +def protocols(): + """Return protocol distribution.""" + events = get_recent_events(2000) + counts = {"TCP": 0, "UDP": 0, "Other": 0} + + for e in events: + proto = e.get("proto", "").upper() + if proto == "TCP": + counts["TCP"] += 1 + elif proto == "UDP": + counts["UDP"] += 1 + else: + counts["Other"] += 1 + + return jsonify(counts) + + +@traffic_bp.route("traffic/bandwidth") +def bandwidth(): + """ + Returns packet count per second for the last ~30 records. + Used for bandwidth line chart. + """ + events = get_recent_events(200) + timeline = {} + + for e in events: + t = e.get("time") + timeline[t] = timeline.get(t, 0) + 1 + + graph = [{"time": k, "value": v} for k, v in timeline.items()] + + return jsonify(graph) diff --git a/backend/sample/bcc_sample.csv b/backend/sample/bcc_sample.csv new file mode 100644 index 0000000000000000000000000000000000000000..ecc0757ad6d31ec53b3f39013f93185f53a22e61 --- /dev/null +++ b/backend/sample/bcc_sample.csv @@ -0,0 +1,2 @@ +proto,src_port,dst_port,flow_duration,total_fwd_pkts,total_bwd_pkts,flags_numeric,payload_len,header_len,rate,iat,syn,ack,rst,fin +6,12345,443,100000,20,5,2,5000,800,50,20000,1,1,0,0 \ No newline at end of file diff --git a/backend/sample/cicids_sample.csv b/backend/sample/cicids_sample.csv new file mode 100644 index 0000000000000000000000000000000000000000..920f3162f9fa28be00bf30cae4da06d7ef17f09e --- /dev/null +++ b/backend/sample/cicids_sample.csv @@ -0,0 +1,2 @@ +Protocol,Dst Port,Flow Duration,Tot Fwd Pkts,Tot Bwd Pkts,TotLen Fwd Pkts,TotLen Bwd Pkts,Fwd Pkt Len Mean,Bwd Pkt Len Mean,Flow IAT Mean,Fwd PSH Flags,Fwd URG Flags,Fwd IAT Mean +6,443,120000,12,2,4000,1500,350,700,60000,1,0,30000 diff --git a/backend/socket_manager.py b/backend/socket_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..8e92a2b90755d1bb5c5a75d6b315e5a75816cc1b --- /dev/null +++ b/backend/socket_manager.py @@ -0,0 +1,80 @@ +# socket_manager.py (Optimized) +# - Non-blocking emit queue with background worker +# - Rate-limited batching for frequent events +# - Backwards-compatible init_socketio & emit_new_event API + +import threading +import time +import queue + +_emit_q = queue.Queue(maxsize=2000) +_socketio = None +_emit_lock = threading.Lock() +_worker_thr = None +_stop_worker = threading.Event() + +# batch/rate config +_BATCH_INTERVAL = 0.5 # seconds between worker sends +_BATCH_MAX = 10 # max events to bundle per emit + + +def init_socketio(socketio): + """Initialize global socketio and start background emit worker.""" + global _socketio, _worker_thr + _socketio = socketio + print("✅ SocketIO initialized (thread-safe)") + if _worker_thr is None or not _worker_thr.is_alive(): + _worker_thr = threading.Thread(target=_emit_worker, daemon=True) + _worker_thr.start() + + +def _emit_worker(): + """Background worker: drains _emit_q and emits aggregated payloads at intervals.""" + last_send = 0.0 + buffer = [] + while not _stop_worker.is_set(): + try: + evt = _emit_q.get(timeout=_BATCH_INTERVAL) + buffer.append(evt) + except Exception: + # timeout, flush if buffer exists + pass + + now = time.time() + if buffer and (now - last_send >= _BATCH_INTERVAL or len(buffer) >= _BATCH_MAX): + payload = {"count": len(buffer), "items": buffer[:_BATCH_MAX]} + try: + if _socketio: + # emit in background so worker isn't blocked on network + _socketio.start_background_task(lambda: _socketio.emit("new_event", payload, namespace="/")) + except Exception as e: + print("⚠️ emit worker error:", e) + buffer.clear() + last_send = now + + # final flush on shutdown + if buffer and _socketio: + try: + _socketio.start_background_task(lambda: _socketio.emit("new_event", {"count": len(buffer), "items": buffer}, namespace="/")) + except Exception: + pass + + +def emit_new_event(evt): + """Enqueue event for background emit. Non-blocking. + + Compatible with previous API: callers can pass full event dicts. + """ + try: + _emit_q.put_nowait(evt) + except queue.Full: + # drop silently (prefer availability over backlog) + return + + +def shutdown_socket_manager(timeout=2): + """Stop background worker gracefully.""" + _stop_worker.set() + if _worker_thr and _worker_thr.is_alive(): + _worker_thr.join(timeout=timeout) + diff --git a/backend/uploads/bcc_sample.csv b/backend/uploads/bcc_sample.csv new file mode 100644 index 0000000000000000000000000000000000000000..ecc0757ad6d31ec53b3f39013f93185f53a22e61 --- /dev/null +++ b/backend/uploads/bcc_sample.csv @@ -0,0 +1,2 @@ +proto,src_port,dst_port,flow_duration,total_fwd_pkts,total_bwd_pkts,flags_numeric,payload_len,header_len,rate,iat,syn,ack,rst,fin +6,12345,443,100000,20,5,2,5000,800,50,20000,1,1,0,0 \ No newline at end of file diff --git a/backend/uploads/cicids_sample.csv b/backend/uploads/cicids_sample.csv new file mode 100644 index 0000000000000000000000000000000000000000..1b3f5a53e98c4141597dd570ab7a6d9eca4e5a77 --- /dev/null +++ b/backend/uploads/cicids_sample.csv @@ -0,0 +1,2 @@ +Protocol,Dst Port,Flow Duration,Tot Fwd Pkts,Tot Bwd Pkts,TotLen Fwd Pkts,TotLen Bwd Pkts,Fwd Pkt Len Mean,Bwd Pkt Len Mean,Flow IAT Mean,Fwd PSH Flags,Fwd URG Flags,Fwd IAT Mean + diff --git a/backend/uploads/cicids_sample_1.csv b/backend/uploads/cicids_sample_1.csv new file mode 100644 index 0000000000000000000000000000000000000000..920f3162f9fa28be00bf30cae4da06d7ef17f09e --- /dev/null +++ b/backend/uploads/cicids_sample_1.csv @@ -0,0 +1,2 @@ +Protocol,Dst Port,Flow Duration,Tot Fwd Pkts,Tot Bwd Pkts,TotLen Fwd Pkts,TotLen Bwd Pkts,Fwd Pkt Len Mean,Bwd Pkt Len Mean,Flow IAT Mean,Fwd PSH Flags,Fwd URG Flags,Fwd IAT Mean +6,443,120000,12,2,4000,1500,350,700,60000,1,0,30000 diff --git a/backend/uploads/iris.csv b/backend/uploads/iris.csv new file mode 100644 index 0000000000000000000000000000000000000000..b7f746072794309a9a971949562a050e7366ceb1 --- /dev/null +++ b/backend/uploads/iris.csv @@ -0,0 +1,151 @@ +150,4,setosa,versicolor,virginica +5.1,3.5,1.4,0.2,0 +4.9,3.0,1.4,0.2,0 +4.7,3.2,1.3,0.2,0 +4.6,3.1,1.5,0.2,0 +5.0,3.6,1.4,0.2,0 +5.4,3.9,1.7,0.4,0 +4.6,3.4,1.4,0.3,0 +5.0,3.4,1.5,0.2,0 +4.4,2.9,1.4,0.2,0 +4.9,3.1,1.5,0.1,0 +5.4,3.7,1.5,0.2,0 +4.8,3.4,1.6,0.2,0 +4.8,3.0,1.4,0.1,0 +4.3,3.0,1.1,0.1,0 +5.8,4.0,1.2,0.2,0 +5.7,4.4,1.5,0.4,0 +5.4,3.9,1.3,0.4,0 +5.1,3.5,1.4,0.3,0 +5.7,3.8,1.7,0.3,0 +5.1,3.8,1.5,0.3,0 +5.4,3.4,1.7,0.2,0 +5.1,3.7,1.5,0.4,0 +4.6,3.6,1.0,0.2,0 +5.1,3.3,1.7,0.5,0 +4.8,3.4,1.9,0.2,0 +5.0,3.0,1.6,0.2,0 +5.0,3.4,1.6,0.4,0 +5.2,3.5,1.5,0.2,0 +5.2,3.4,1.4,0.2,0 +4.7,3.2,1.6,0.2,0 +4.8,3.1,1.6,0.2,0 +5.4,3.4,1.5,0.4,0 +5.2,4.1,1.5,0.1,0 +5.5,4.2,1.4,0.2,0 +4.9,3.1,1.5,0.2,0 +5.0,3.2,1.2,0.2,0 +5.5,3.5,1.3,0.2,0 +4.9,3.6,1.4,0.1,0 +4.4,3.0,1.3,0.2,0 +5.1,3.4,1.5,0.2,0 +5.0,3.5,1.3,0.3,0 +4.5,2.3,1.3,0.3,0 +4.4,3.2,1.3,0.2,0 +5.0,3.5,1.6,0.6,0 +5.1,3.8,1.9,0.4,0 +4.8,3.0,1.4,0.3,0 +5.1,3.8,1.6,0.2,0 +4.6,3.2,1.4,0.2,0 +5.3,3.7,1.5,0.2,0 +5.0,3.3,1.4,0.2,0 +7.0,3.2,4.7,1.4,1 +6.4,3.2,4.5,1.5,1 +6.9,3.1,4.9,1.5,1 +5.5,2.3,4.0,1.3,1 +6.5,2.8,4.6,1.5,1 +5.7,2.8,4.5,1.3,1 +6.3,3.3,4.7,1.6,1 +4.9,2.4,3.3,1.0,1 +6.6,2.9,4.6,1.3,1 +5.2,2.7,3.9,1.4,1 +5.0,2.0,3.5,1.0,1 +5.9,3.0,4.2,1.5,1 +6.0,2.2,4.0,1.0,1 +6.1,2.9,4.7,1.4,1 +5.6,2.9,3.6,1.3,1 +6.7,3.1,4.4,1.4,1 +5.6,3.0,4.5,1.5,1 +5.8,2.7,4.1,1.0,1 +6.2,2.2,4.5,1.5,1 +5.6,2.5,3.9,1.1,1 +5.9,3.2,4.8,1.8,1 +6.1,2.8,4.0,1.3,1 +6.3,2.5,4.9,1.5,1 +6.1,2.8,4.7,1.2,1 +6.4,2.9,4.3,1.3,1 +6.6,3.0,4.4,1.4,1 +6.8,2.8,4.8,1.4,1 +6.7,3.0,5.0,1.7,1 +6.0,2.9,4.5,1.5,1 +5.7,2.6,3.5,1.0,1 +5.5,2.4,3.8,1.1,1 +5.5,2.4,3.7,1.0,1 +5.8,2.7,3.9,1.2,1 +6.0,2.7,5.1,1.6,1 +5.4,3.0,4.5,1.5,1 +6.0,3.4,4.5,1.6,1 +6.7,3.1,4.7,1.5,1 +6.3,2.3,4.4,1.3,1 +5.6,3.0,4.1,1.3,1 +5.5,2.5,4.0,1.3,1 +5.5,2.6,4.4,1.2,1 +6.1,3.0,4.6,1.4,1 +5.8,2.6,4.0,1.2,1 +5.0,2.3,3.3,1.0,1 +5.6,2.7,4.2,1.3,1 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+6.5,3.0,5.2,2.0,2 +6.2,3.4,5.4,2.3,2 +5.9,3.0,5.1,1.8,2 diff --git a/backend/utils/ai_engine.py b/backend/utils/ai_engine.py new file mode 100644 index 0000000000000000000000000000000000000000..43f77d82c5712477ffa73aa8458a8f55ea63690f --- /dev/null +++ b/backend/utils/ai_engine.py @@ -0,0 +1,164 @@ +# utils/ai_engine.py +# ----------------------------------------- +# Lightweight "AI" engine using rules + templates +# No heavy ML model – safe for your laptop 🙂 + +from collections import Counter +from datetime import datetime + + +def _normalize_label(label: str) -> str: + return str(label or "Unknown").strip().upper() + + +# 1️⃣ Explain a single threat/event +def explain_threat(event: dict) -> str: + """ + Takes a single event dict (from logger / recent()) + and returns a human-readable explanation. + """ + + label = _normalize_label(event.get("prediction")) + risk_level = str(event.get("risk_level", "Low")).title() + src_ip = event.get("src_ip") or event.get("src") or "Unknown source" + dst_ip = event.get("dst_ip") or event.get("dst") or "Unknown destination" + proto = event.get("proto", "Unknown") + sport = event.get("sport") or event.get("src_port") or "?" + dport = event.get("dport") or event.get("dst_port") or "?" + + # Simple knowledge base + explanations = { + "VPN": ( + "Traffic from {src} to {dst} over {proto} looks like VPN usage. " + "VPN tunnels encrypt traffic and can hide the real origin of an attacker. " + "Review if this VPN endpoint is expected for this host." + ), + "TOR": ( + "Traffic appears to be routed through the Tor anonymity network. " + "Tor is commonly used to hide attacker identity. " + "Investigate the host at {src} and check if Tor usage is allowed." + ), + "I2P": ( + "Detected I2P (Invisible Internet Project) style traffic. " + "I2P is an anonymity network similar to Tor and can be abused for C2 channels." + ), + "FREENET": ( + "Traffic resembles Freenet P2P anonymity network. " + "Such networks can be used to exchange illegal or malicious content." + ), + "ZERONET": ( + "ZeroNet-like traffic detected. ZeroNet hosts sites over a P2P network. " + "This may bypass normal web filtering and logging." + ), + # CICIDS-style examples – extend as you like + "DOS HULK": ( + "High-rate HTTP traffic typical of DoS-Hulk attack was detected. " + "This can exhaust web server resources and cause service disruption." + ), + "DOS SLOWLORIS": ( + "Slowloris-style DoS traffic detected. It keeps many HTTP connections open " + "to slowly exhaust server connection limits." + ), + "BOT": ( + "Behavior suggests the host may be part of a botnet. " + "Correlate with outbound connections and run malware scans on {src}." + ), + "BENIGN": ( + "This flow is classified as BENIGN. No immediate malicious pattern detected, " + "but you should still monitor for anomalies over time." + ), + } + + # Pick best match (exact or substring) + text = None + if label in explanations: + text = explanations[label] + else: + for k, v in explanations.items(): + if k in label: + text = v + break + + if text is None: + text = ( + "The traffic is classified as '{label}' with a risk level of {risk}. " + "Review source {src} → destination {dst}, protocol {proto}, " + "and ports {sport} → {dport} for suspicious patterns." + ) + + return text.format( + label=label, + risk=risk_level, + src=src_ip, + dst=dst_ip, + proto=proto, + sport=sport, + dport=dport, + ) + + +# 2️⃣ Summarize multiple events (for report) +def summarize_events(events, model: str = "bcc") -> str: + """ + Takes a list of events and returns a high-level English summary. + """ + + if not events: + return "No recent events available for summary." + + labels = [_normalize_label(e.get("prediction")) for e in events] + counts = Counter(labels) + total = len(events) + + high_risk_keywords = [ + "DDOS", "DOS", "BRUTE", "SQL", "BOT", "INFILTRATION", "HULK", + "SLOWLORIS", "SLOWHTTPTEST" + ] + high_risk = sum( + c for lbl, c in counts.items() + if any(k in lbl for k in high_risk_keywords) + ) + + tor_like = sum( + counts.get(lbl, 0) for lbl in ["TOR", "I2P", "ZERONET", "FREENET", "VPN"] + ) + + ts = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + + # Build readable summary + parts = [ + f"AI Summary generated at {ts} for model '{model.upper()}'.", + f"Total analysed events: {total}.", + ] + + if high_risk: + parts.append( + f"High-risk attacks detected: {high_risk} events " + f"({', '.join(k for k in counts.keys() if any(x in k for x in high_risk_keywords))})." + ) + else: + parts.append("No high-risk attack pattern strongly detected in this window.") + + if tor_like: + parts.append( + f"Anonymity or tunneling traffic (VPN/TOR/I2P/etc.) observed in {tor_like} events. " + "Verify if this usage is expected and authorized." + ) + + # top 3 labels + top3 = counts.most_common(3) + label_str = ", ".join(f"{lbl}: {cnt}" for lbl, cnt in top3) + parts.append(f"Top traffic classes: {label_str}.") + + if model == "bcc": + parts.append( + "BCC model focuses on live packet patterns; consider correlating with host logs " + "for deeper forensic analysis." + ) + else: + parts.append( + "CICIDS model analyses flow-level statistics; consider exporting flows for " + "offline investigation if anomalies increase." + ) + + return " ".join(parts) diff --git a/backend/utils/geo_lookup.py b/backend/utils/geo_lookup.py new file mode 100644 index 0000000000000000000000000000000000000000..ccd4d34252ac3fe2d775eb6828da157153c934bd --- /dev/null +++ b/backend/utils/geo_lookup.py @@ -0,0 +1,133 @@ +# utils/geo_lookup.py +# ========================================== +# 🌍 GEO LOOKUP UTILITY — Robust version +# - Uses ipwho.is +# - Validates inputs +# - Caches results +# - Graceful fallback for bad/ private IPs +# ========================================== + +import requests +from functools import lru_cache +import re +import time + +# Public API (no API key) +GEO_API = "https://ipwho.is/{ip}" + +# Regex for private/reserved IPv4 blocks + simple IPv4/IPv6 check +_IPV4_RE = re.compile(r"^(?:\d{1,3}\.){3}\d{1,3}$") +_IPV6_RE = re.compile(r"^[0-9a-fA-F:]+$") + +PRIVATE_IP_RANGES = [ + re.compile(r"^127\."), # localhost + re.compile(r"^10\."), # private + re.compile(r"^192\.168\."), # private + re.compile(r"^172\.(1[6-9]|2[0-9]|3[0-1])\."), # private block + re.compile(r"^0\."), # invalid + re.compile(r"^255\."), # broadcast/reserved +] + +# Cache size tuned to common usage (increase if you have many distinct IPs) +@lru_cache(maxsize=2000) +def get_geo_info(ip: str) -> dict: + """Return geolocation info for an IP address (string-safe, cached, fallback).""" + # Normalize + try: + ip_raw = ip + if ip is None: + return _default_geo(ip, "Empty IP") + ip = str(ip).strip() + except Exception: + return _default_geo(ip, "Invalid IP") + + # Quick checks + if ip == "" or ip.lower() in ("unknown", "n/a", "na", "local", "localhost"): + return _default_geo(ip, "Unknown") + + # If it's clearly not an IPv4/IPv6 string, avoid calling external API + if not (_IPV4_RE.match(ip) or _IPV6_RE.match(ip)): + return _default_geo(ip, "Not an IP") + + # Private/reserved check + if any(r.match(ip) for r in PRIVATE_IP_RANGES): + return { + "ip": ip, + "country": "Local", + "city": "Private Network", + "lat": 0.0, + "lon": 0.0, + } + + # Query remote API (with timeout + basic retry) + try: + # simple single attempt with timeout; if you need reliability add a tiny backoff/retry + res = requests.get(GEO_API.format(ip=ip), timeout=4) + if res.status_code == 200: + data = res.json() + # ipwho.is returns {"success": false, "message": "..."} for invalid + if data.get("success", True) is False: + return _default_geo(ip, data.get("message", "Invalid IP")) + return { + "ip": ip, + "country": data.get("country", "Unknown"), + "city": data.get("city", "Unknown"), + "lat": float(data.get("latitude") or 0.0), + "lon": float(data.get("longitude") or 0.0), + } + # non-200 -> fallback + print(f"⚠️ Geo lookup failed for {ip} (status {res.status_code})") + except Exception as e: + # network errors, DNS issues, etc. + print(f"⚠️ Geo lookup error for {ip}: {e}") + + return _default_geo(ip, "Unknown") + + +def _default_geo(ip: str, reason="Unknown"): + """Return default location info when lookup fails.""" + return { + "ip": ip, + "country": reason, + "city": "Unknown", + "lat": 0.0, + "lon": 0.0, + } + + +def enrich_event_with_geo(evt: dict) -> dict: + """ + Given an event dict that contains 'src_ip' and 'dst_ip' (or similar keys), + attach src/dst city, country, lat, lon fields. + This function is safe to call synchronously, but consider async enrichment + when running on a hot packet-processing loop (see optional snippet below). + """ + try: + # Accept multiple possible keys (compatibility) + src_ip = evt.get("src_ip") or evt.get("src") or evt.get("srcIP") or "" + dst_ip = evt.get("dst_ip") or evt.get("dst") or evt.get("dstIP") or "" + + # Normalize to string before calling get_geo_info + src_ip = str(src_ip).strip() if src_ip is not None else "" + dst_ip = str(dst_ip).strip() if dst_ip is not None else "" + + # Get geo info (cached) + src_info = get_geo_info(src_ip) + dst_info = get_geo_info(dst_ip) + + evt.update({ + "src_country": src_info["country"], + "dst_country": dst_info["country"], + "src_city": src_info["city"], + "dst_city": dst_info["city"], + "src_lat": src_info["lat"], + "src_lon": src_info["lon"], + "dst_lat": dst_info["lat"], + "dst_lon": dst_info["lon"], + }) + except Exception as e: + # Keep it quiet but informative + print(f"⚠️ Geo enrichment failed for event: {e}") + + return evt + diff --git a/backend/utils/logger.py b/backend/utils/logger.py new file mode 100644 index 0000000000000000000000000000000000000000..b726866ee90cd9bcaecc05a5847cef48db11f617 --- /dev/null +++ b/backend/utils/logger.py @@ -0,0 +1,273 @@ +# logger.py (Model-separated, non-blocking logger, per-model CSVs) +# ------------------------------------------------------------- +import os +import csv +import threading +import time +from datetime import datetime +import numpy as np + +LOG_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "logs")) +os.makedirs(LOG_DIR, exist_ok=True) + +BCC_LOG_FILE = os.path.join(LOG_DIR, "bcc_logs.csv") +CICIDS_LOG_FILE = os.path.join(LOG_DIR, "cicids_logs.csv") + +_MAX_RECENT = 500 +_FLUSH_INTERVAL = 2.0 +_FLUSH_BATCH = 50 + +_headers = [ + "time", "src_ip", "sport", "dst_ip", "dport", "proto", + "prediction", "risk_level", "risk_score", + "src_country", "src_city", "src_lat", "src_lon", + "dst_country", "dst_city", "dst_lat", "dst_lon" +] + +# In-memory per-model buffers & stats +_model_events = { + "bcc": [], # list of dicts + "cicids": [] +} + +_model_stats = { + "bcc": {}, + "cicids": {} +} + +# active model (default) +_active_model_lock = threading.Lock() +_active_model = "bcc" + +# writer buffers and locks +_write_buffer = [] # list of dicts, each item must include "model" key +_buffer_lock = threading.Lock() +_events_lock = threading.Lock() + +_stop_writer = threading.Event() + +# ------------------------- +# Helpers: file name for model +# ------------------------- +def _file_for_model(model): + if model == "cicids": + return CICIDS_LOG_FILE + return BCC_LOG_FILE + +# ------------------------- +# Full overwrite for a model CSV +# ------------------------- +def _flush_full_overwrite_model(model): + """Rewrite the entire CSV for a specific model from its in-memory buffer.""" + fname = _file_for_model(model) + try: + with _events_lock: + rows = list(_model_events.get(model, [])) + with open(fname, "w", newline="", encoding="utf-8") as f: + writer = csv.DictWriter(f, fieldnames=_headers) + writer.writeheader() + for row in rows: + writer.writerow({k: row.get(k, "") for k in _headers}) + # optional debug print + # print(f"[logger] {model} CSV fully rewritten: {len(rows)} rows -> {fname}") + except Exception as e: + print("[logger] Full overwrite failed:", e) + +# ------------------------- +# Flush small batches to disk (append) +# ------------------------- +def _flush_to_disk(): + global _write_buffer + with _buffer_lock: + if not _write_buffer: + return + batch = _write_buffer[:_FLUSH_BATCH] + _write_buffer = _write_buffer[len(batch):] + + # group by model for efficient writes + groups = {} + for row in batch: + m = row.get("model", "bcc") + groups.setdefault(m, []).append(row) + + for model, rows in groups.items(): + fname = _file_for_model(model) + try: + file_empty = not os.path.exists(fname) or os.stat(fname).st_size == 0 + with open(fname, "a", newline="", encoding="utf-8") as f: + writer = csv.DictWriter(f, fieldnames=_headers) + if file_empty: + writer.writeheader() + for r in rows: + # write only header keys (ignore extra) + writer.writerow({k: r.get(k, "") for k in _headers}) + except Exception as e: + print("[logger] Append write error for", model, ":", e) + +# ------------------------- +# Background writer thread +# ------------------------- +def _writer_thread(): + while not _stop_writer.is_set(): + time.sleep(_FLUSH_INTERVAL) + _flush_to_disk() + # flush remaining on shutdown + _flush_to_disk() + +_writer_thr = threading.Thread(target=_writer_thread, daemon=True) +_writer_thr.start() + +# ------------------------- +# Load existing CSVs into _model_events on startup (keep last _MAX_RECENT) +# ------------------------- +def _load_recent_model(model): + fname = _file_for_model(model) + if not os.path.exists(fname): + return [] + try: + with open(fname, "r", encoding="utf-8") as f: + reader = list(csv.DictReader(f)) + return reader[-_MAX_RECENT:] + except Exception: + return [] + +def _load_all_recent(): + global _model_events + with _events_lock: + _model_events["bcc"] = _load_recent_model("bcc") + _model_events["cicids"] = _load_recent_model("cicids") + +_load_all_recent() + +# =============================== +# Public API: push_event +# =============================== +def push_event(evt): + """ + evt: dict containing event fields expected (prediction, src_ip, dst_ip, etc.) + Uses current active model to store event. + Also enqueues to write buffer for background flush. + """ + global _write_buffer + + # attach model at time of push + with _active_model_lock: + model = _active_model + + e = dict(evt) + e.setdefault("time", datetime.now().strftime("%H:%M:%S")) + e.setdefault("risk_level", "Low") + e.setdefault("risk_score", 0) + + # add to in-memory buffer for model + with _events_lock: + _model_events.setdefault(model, []) + _model_events[model].append(e) + if len(_model_events[model]) > _MAX_RECENT: + _model_events[model] = _model_events[model][-_MAX_RECENT:] + + # update stats + pred = str(e.get("prediction", "Unknown")) + _model_stats.setdefault(model, {}) + _model_stats[model][pred] = _model_stats[model].get(pred, 0) + 1 + + # add to write buffer with model tag for background writer + item = dict(e) + item["model"] = model + with _buffer_lock: + _write_buffer.append(item) + # if buffer grows big, flush asynchronously + if len(_write_buffer) > (_FLUSH_BATCH * 4): + threading.Thread(target=_flush_to_disk, daemon=True).start() + +# =============================== +# Public API: get recent & stats +# =============================== +def get_recent_events(model="bcc", n=None): + with _events_lock: + data = list(_model_events.get(model, [])) + if n: + return data[-n:] + return data + +def get_model_stats(model="bcc"): + with _events_lock: + # return a shallow copy to avoid external mutation + return dict(_model_stats.get(model, {})) + +# ------------------------- +# Convenience: summary across active model (legacy) +# ------------------------- +def summarize_counts(): + with _active_model_lock: + model = _active_model + return get_model_stats(model) + +# =============================== +# Model selection API +# =============================== +def set_active_model(model): + if model not in ("bcc", "cicids"): + raise ValueError("invalid model") + with _active_model_lock: + global _active_model + _active_model = model + # no immediate clearing — in-memory buffers persist per model + return _active_model + +def get_active_model(): + with _active_model_lock: + return _active_model + +# =============================== +# CLEAR / DELETE (model-wise) +# =============================== +def clear_last_events(model="bcc", n=99999): + with _events_lock: + ev = _model_events.get(model, []) + if n >= len(ev): + _model_events[model] = [] + else: + _model_events[model] = ev[:-n] + # reset stats for this model + _model_stats[model] = {} + # rewrite model CSV fully + _flush_full_overwrite_model(model) + return True + +def delete_by_index(model="bcc", idx=0): + with _events_lock: + ev = _model_events.get(model, []) + if 0 <= idx < len(ev): + ev.pop(idx) + _model_events[model] = ev + # recompute stats (simple recompute) + _model_stats[model] = {} + for e in ev: + pred = str(e.get("prediction", "Unknown")) + _model_stats[model][pred] = _model_stats[model].get(pred, 0) + 1 + _flush_full_overwrite_model(model) + return True + return False + +def delete_by_prediction(model="bcc", pred=None): + if pred is None: + return False + with _events_lock: + ev = _model_events.get(model, []) + _model_events[model] = [e for e in ev if e.get("prediction") != pred] + # recompute stats + _model_stats[model] = {} + for e in _model_events[model]: + p = str(e.get("prediction", "Unknown")) + _model_stats[model][p] = _model_stats[model].get(p, 0) + 1 + _flush_full_overwrite_model(model) + return True + +# =============================== +# Shutdown +# =============================== +def shutdown_logger(): + _stop_writer.set() + _writer_thr.join(timeout=3) + diff --git a/backend/utils/model_selector.py b/backend/utils/model_selector.py new file mode 100644 index 0000000000000000000000000000000000000000..b402b34614423c082748175e978ffe430e50c0c8 --- /dev/null +++ b/backend/utils/model_selector.py @@ -0,0 +1,116 @@ +import os +import joblib +import threading +import traceback + +# Global active model (default = bcc so your current flow remains unchanged) +ACTIVE_MODEL = "bcc" +_ACTIVE_LOCK = threading.Lock() + +# Cache loaded models to avoid repeated disk loads +_MODEL_CACHE = {} +ML_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "ml_models")) +print("[model_selector] ML_DIR =", ML_DIR) +try: + print("[model_selector] ML_DIR files:", os.listdir(ML_DIR)) +except Exception as e: + print("[model_selector] Could not list ML_DIR:", e) + + +def _try_load(path): + """Try to joblib.load(path). On failure return None but print full traceback.""" + if not os.path.exists(path): + print(f"[model_selector] SKIP (not found): {path}") + return None + try: + print(f"[model_selector] Attempting to load: {path}") + obj = joblib.load(path) + print(f"[model_selector] Successfully loaded: {os.path.basename(path)}") + return obj + except Exception as e: + print(f"[model_selector] FAILED to load {path}: {e}") + traceback.print_exc() + return None + +def load_model(model_key): + """Return a dict with keys depending on model. Caches result.""" + if model_key in _MODEL_CACHE: + return _MODEL_CACHE[model_key] + + if model_key == "bcc": + # original BCC artifact names (your working files) + model_path = os.path.join(ML_DIR, "realtime_model.pkl") + scaler_path = os.path.join(ML_DIR, "realtime_scaler.pkl") + encoder_path = os.path.join(ML_DIR, "realtime_encoder.pkl") + + model = _try_load(model_path) + scaler = _try_load(scaler_path) + encoder = _try_load(encoder_path) + + if model is None: + print(f"[model_selector] WARNING: bcc model not found at {model_path}") + _MODEL_CACHE["bcc"] = {"model": model, "scaler": scaler, "encoder": encoder} + return _MODEL_CACHE["bcc"] + + if model_key == "cicids": + # Prefer the RF pipeline you requested; try common names in preferred order + candidate_models = [ + "rf_pipeline.joblib", # preferred - your RF pipeline + "cicids_rf.joblib", + "rf_pipeline.pkl", + "cicids_model.joblib", + "lgb_pipeline.joblib", + "cicids_rf.pkl", + ] + # prefer 'training_artifacts' or 'cicids_artifacts' + candidate_artifacts = [ + "training_artifacts.joblib", + "training_artifacts.pkl", + "cicids_artifacts.joblib", + "cicids_artifacts.pkl", + "artifacts.joblib", + "artifacts.pkl" + ] + + model = None + artifacts = None + for fn in candidate_models: + p = os.path.join(ML_DIR, fn) + model = _try_load(p) + if model is not None: + print(f"[model_selector] Loaded cicids model from {p}") + break + + for fn in candidate_artifacts: + p = os.path.join(ML_DIR, fn) + artifacts = _try_load(p) + if artifacts is not None: + print(f"[model_selector] Loaded cicids artifacts from {p}") + break + + if model is None: + print("[model_selector] WARNING: No cicids model found in ml_models.") + if artifacts is None: + print("[model_selector] WARNING: No cicids artifacts found in ml_models.") + + # artifacts expected to include: 'scaler' and 'features' at minimum + _MODEL_CACHE["cicids"] = { + "model": model, + "artifacts": artifacts + } + return _MODEL_CACHE["cicids"] + + raise ValueError("Unknown model_key") + +def set_active_model(key: str): + global ACTIVE_MODEL + if key not in ("bcc", "cicids"): + raise ValueError("Active model must be 'bcc' or 'cicids'") + with _ACTIVE_LOCK: + ACTIVE_MODEL = key + print(f"[model_selector] ACTIVE_MODEL set to: {ACTIVE_MODEL}") + +def get_active_model(): + return ACTIVE_MODEL + + diff --git a/backend/utils/pcap_to_csv.py b/backend/utils/pcap_to_csv.py new file mode 100644 index 0000000000000000000000000000000000000000..b42021ad15c39e154cc1c0a880a24785a124246f --- /dev/null +++ b/backend/utils/pcap_to_csv.py @@ -0,0 +1,23 @@ +import pandas as pd +from scapy.all import rdpcap + +def convert_pcap_to_csv(input_pcap): + packets = rdpcap(input_pcap) + data = [] + + for pkt in packets: + try: + row = { + "src_port": pkt.sport if hasattr(pkt, "sport") else 0, + "dst_port": pkt.dport if hasattr(pkt, "dport") else 0, + "proto": pkt.proto if hasattr(pkt, "proto") else 0, + "payload_len": len(pkt.payload) + } + data.append(row) + except: + pass + + df = pd.DataFrame(data) + out_csv = input_pcap + ".csv" + df.to_csv(out_csv, index=False) + return out_csv diff --git a/backend/utils/risk_engine.py b/backend/utils/risk_engine.py new file mode 100644 index 0000000000000000000000000000000000000000..633322e2f5629ac6e45288a8850b8275db5fe2d8 --- /dev/null +++ b/backend/utils/risk_engine.py @@ -0,0 +1,77 @@ +# risk_engine.py (Optimized) +# - Accepts optional `recent_events` to avoid repeated disk/IO calls +# - Uses light-weight counters and caching for frequency checks +# - Returns (level, score) as before + +import random +import time +from utils.logger import get_recent_events + +# small in-memory cache for source counts to avoid repeated scans +_SRC_CACHE = { + "ts": 0, + "counts": {}, + "ttl": 2.0 # seconds +} + + +def _build_source_cache(recent_events): + counts = {} + for e in recent_events: + s = e.get("src_ip") + if s: + counts[s] = counts.get(s, 0) + 1 + return counts + + +def compute_risk_score(evt, recent_events=None): + """Compute adaptive risk score (0–100). + + If `recent_events` is provided, it is used directly. Otherwise `get_recent_events()` + is called once (limited inside the function). + """ + label = (evt.get("prediction") or "").upper() + src_ip = evt.get("src_ip") or "" + + base_map = { + "TOR": 90, + "I2P": 85, + "ZERONET": 70, + "VPN": 55, + "FREENET": 60, + "HTTP": 30, + "DNS": 25, + } + base = base_map.get(label, 35) + + # get recent events once if not provided + if recent_events is None: + recent_events = get_recent_events() + + # try cached counts for short TTL + now = time.time() + if now - _SRC_CACHE.get("ts", 0) > _SRC_CACHE.get("ttl", 2.0) or not _SRC_CACHE.get("counts"): + _SRC_CACHE["counts"] = _build_source_cache(recent_events) + _SRC_CACHE["ts"] = now + + freq = _SRC_CACHE["counts"].get(src_ip, 0) + + freq_boost = 0 + if freq >= 3: + freq_boost = 5 + if freq >= 6: + freq_boost = 15 + + noise = random.randint(-3, 3) + + score = min(100, max(0, base + freq_boost + noise)) + + if score >= 80: + level = "High" + elif score >= 50: + level = "Medium" + else: + level = "Low" + + return level, score + diff --git a/frontend/.gitignore b/frontend/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..a547bf36d8d11a4f89c59c144f24795749086dd1 --- /dev/null +++ b/frontend/.gitignore @@ -0,0 +1,24 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/frontend/README.md b/frontend/README.md new file mode 100644 index 0000000000000000000000000000000000000000..18bc70ebe277fbfe6e55e6f9a0ae7e2c3e4bdd83 --- /dev/null +++ b/frontend/README.md @@ -0,0 +1,16 @@ +# React + Vite + +This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules. + +Currently, two official plugins are available: + +- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Babel](https://babeljs.io/) (or [oxc](https://oxc.rs) when used in [rolldown-vite](https://vite.dev/guide/rolldown)) for Fast Refresh +- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh + +## React Compiler + +The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation). + +## Expanding the ESLint configuration + +If you are developing a production application, we recommend using TypeScript with type-aware lint rules enabled. Check out the [TS template](https://github.com/vitejs/vite/tree/main/packages/create-vite/template-react-ts) for information on how to integrate TypeScript and [`typescript-eslint`](https://typescript-eslint.io) in your project. diff --git a/frontend/components.json b/frontend/components.json new file mode 100644 index 0000000000000000000000000000000000000000..2d14f31ca9f026e568ff1c9e76abeef4a9251d3c --- /dev/null +++ b/frontend/components.json @@ -0,0 +1,22 @@ +{ + "$schema": "https://ui.shadcn.com/schema.json", + "style": "default", + "rsc": false, + "tsx": false, + "tailwind": { + "config": "tailwind.config.js", + "css": "src/index.css", + "baseColor": "slate", + "cssVariables": true, + "prefix": "" + }, + "iconLibrary": "lucide", + "aliases": { + "components": "@/components", + "utils": "@/lib/utils", + "ui": "@/components/ui", + "lib": "@/lib", + "hooks": "@/hooks" + }, + "registries": {} +} diff --git a/frontend/eslint.config.js b/frontend/eslint.config.js new file mode 100644 index 0000000000000000000000000000000000000000..cee1e2c788d908e4a8ff796eb6edbe73ee73fffd --- /dev/null +++ b/frontend/eslint.config.js @@ -0,0 +1,29 @@ +import js from '@eslint/js' +import globals from 'globals' +import reactHooks from 'eslint-plugin-react-hooks' +import reactRefresh from 'eslint-plugin-react-refresh' +import { defineConfig, globalIgnores } from 'eslint/config' + +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{js,jsx}'], + extends: [ + js.configs.recommended, + reactHooks.configs['recommended-latest'], + reactRefresh.configs.vite, + ], + languageOptions: { + ecmaVersion: 2020, + globals: globals.browser, + parserOptions: { + ecmaVersion: 'latest', + ecmaFeatures: { jsx: true }, + sourceType: 'module', + }, + }, + rules: { + 'no-unused-vars': ['error', { varsIgnorePattern: '^[A-Z_]' }], + }, + }, +]) diff --git a/frontend/index.html b/frontend/index.html new file mode 100644 index 0000000000000000000000000000000000000000..a05cd5a5b0c8260b24934b37dc9f55b422a5f6e0 --- /dev/null +++ b/frontend/index.html @@ -0,0 +1,17 @@ + + + + + + + +NIDS Cyber Security + + +
+ + + \ No newline at end of file diff --git a/frontend/jsconfig.json b/frontend/jsconfig.json new file mode 100644 index 0000000000000000000000000000000000000000..cd326945a5dcc179c970154b1c6a28e13ea75a2b --- /dev/null +++ b/frontend/jsconfig.json @@ -0,0 +1,8 @@ +{ + "compilerOptions": { + "baseUrl": "./src", + "paths": { + "@/*": ["*"] + } + } +} diff --git a/frontend/package-lock.json b/frontend/package-lock.json new file mode 100644 index 0000000000000000000000000000000000000000..d1a33b800dd6acaf643b842cd18d84912b8ea969 --- /dev/null +++ b/frontend/package-lock.json @@ -0,0 +1,8099 @@ +{ + "name": "frontend", + "version": "0.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "frontend", + "version": "0.0.0", + "dependencies": { + "@radix-ui/react-accordion": "^1.2.12", + "@radix-ui/react-scroll-area": "^1.2.10", + "@radix-ui/react-separator": "^1.1.8", + "@radix-ui/react-slot": "^1.2.4", + "@radix-ui/react-switch": "^1.2.6", + 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"peerDependencies": { + "@types/react": ">=18.0.0", + "immer": ">=9.0.6", + "react": ">=18.0.0", + "use-sync-external-store": ">=1.2.0" + }, + "peerDependenciesMeta": { + "@types/react": { + "optional": true + }, + "immer": { + "optional": true + }, + "react": { + "optional": true + }, + "use-sync-external-store": { + "optional": true + } + } + } + } +} diff --git a/frontend/package.json b/frontend/package.json new file mode 100644 index 0000000000000000000000000000000000000000..9cc6b8f583debe3f1ae4a3312c8c978eb31201ea --- /dev/null +++ b/frontend/package.json @@ -0,0 +1,64 @@ +{ + "name": "frontend", + "private": true, + "version": "0.0.0", + "type": "module", + "scripts": { + "dev": "vite", + "build": "vite build", + "lint": "eslint .", + "preview": "vite preview", + "tailwind-init": "tailwindcss init -p" + }, + "dependencies": { + "@radix-ui/react-accordion": "^1.2.12", + "@radix-ui/react-scroll-area": "^1.2.10", + "@radix-ui/react-separator": "^1.1.8", + "@radix-ui/react-slot": "^1.2.4", + "@radix-ui/react-switch": "^1.2.6", + "@radix-ui/react-tabs": "^1.1.13", + "@react-three/drei": "^10.7.6", + "@react-three/fiber": "^9.4.0", + "axios": "^1.13.1", + "class-variance-authority": "^0.7.1", + "clsx": "^2.1.1", + "firebase": "^12.5.0", + "framer-motion": "^12.23.24", + "html2canvas": "^1.4.1", + "jspdf": "^3.0.3", + "leaflet": "^1.9.4", + "lodash": "^4.17.21", + "lucide-react": "^0.552.0", + "prop-types": "^15.8.1", + "react": "^19.1.1", + "react-circular-progressbar": "^2.2.0", + "react-dom": "^19.1.1", + "react-draggable": "^4.5.0", + "react-gauge-chart": "^0.5.1", + "react-hot-toast": "^2.6.0", + "react-is": "^19.2.0", + "react-leaflet": "^5.0.0", + "react-parallax-tilt": "^1.7.313", + "react-router-dom": "^7.9.5", + "react-simple-maps": "^3.0.0", + "recharts": "^3.3.0", + "tailwind-merge": "^3.4.0", + "tailwindcss-animate": "^1.0.7", + "three": "^0.181.1", + "xlsx": "^0.18.5" + }, + "devDependencies": { + "@eslint/js": "^9.36.0", + "@types/react": "^19.1.16", + "@types/react-dom": "^19.1.9", + "@vitejs/plugin-react": "^5.0.4", + "autoprefixer": "^10.4.21", + "eslint": "^9.36.0", + "eslint-plugin-react-hooks": "^5.2.0", + "eslint-plugin-react-refresh": "^0.4.22", + "globals": "^16.4.0", + "postcss": "^8.5.6", + "tailwindcss": "^3.4.18", + "vite": "^7.1.7" + } +} diff --git a/frontend/postcss.config.js b/frontend/postcss.config.js new file mode 100644 index 0000000000000000000000000000000000000000..e735cfc417522abfb47d80ef294bd72ae9418eca --- /dev/null +++ b/frontend/postcss.config.js @@ -0,0 +1,6 @@ +export default { +plugins: { +tailwindcss: {}, +autoprefixer: {}, +}, +}; \ No newline at end of file diff --git a/frontend/public/images.ico b/frontend/public/images.ico new file mode 100644 index 0000000000000000000000000000000000000000..e8a09a7cab7ed3ec71ceee48cc3756ee00951d78 Binary files /dev/null and b/frontend/public/images.ico differ diff --git a/frontend/public/vite.svg b/frontend/public/vite.svg new file mode 100644 index 0000000000000000000000000000000000000000..e7b8dfb1b2a60bd50538bec9f876511b9cac21e3 --- /dev/null +++ b/frontend/public/vite.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/App.jsx b/frontend/src/App.jsx new file mode 100644 index 0000000000000000000000000000000000000000..eca0b1573a2d33dba38a7066f8a443df2749d10c --- /dev/null +++ b/frontend/src/App.jsx @@ -0,0 +1,80 @@ +import { BrowserRouter as Router, Routes, Route, useLocation } from "react-router-dom"; +import { LiveDataProvider } from "./context/DataContext"; +import { AuthProvider } from "./context/AuthContext"; +import ProtectedRoute from "./components/ProtectedRoute"; +import Sidebar from "./components/Sidebar"; +import { useState } from "react"; + +import AuthPage from "./pages/Login"; +import Dashboard from "./pages/Dashboard"; +import LiveTraffic from "./components/dashboard/LiveDashboard"; +import FlowPage from "./pages/FlowPage"; +import SettingsPage from "./pages/SettingsPage"; +import InfoPage from "./pages/InfoPage"; +import ThreatIntel from "./pages/ThreatIntel"; +import Reports from "./pages/ReportsPage"; +import TrafficPage from "./pages/TrafficPage"; +import IncidentsPage from "./pages/IncidentsPage"; +import SystemPage from "./pages/SystemPage"; +import ResponsePage from "./pages/ResponsePage"; +import MlmodelPage from "./pages/MLModelsPage"; +import ConstellationBackground from "./components/ConstellationBackground"; +import SamplePred from "./pages/MLAttackSamplesPage"; +import Aichat from "./pages/ChatAssistant"; +import MainLayout from "./components/MainLayout"; + + +// Layout wrapper +function AppLayout() { + const location = useLocation(); + const hideSidebar = location.pathname === "/login"; + + const [collapsed, setCollapsed] = useState(false); + + return ( +
+ + + {!hideSidebar && ( + + )} + +
+ + } /> + } /> + } /> + } /> + } /> + } /> + } /> + } /> + } /> + } /> + } /> + } /> + } /> + } /> + } /> + +
+
+ ); +} + +export default function App() { + return ( + + + + + + + + ); +} + diff --git a/frontend/src/api.js b/frontend/src/api.js new file mode 100644 index 0000000000000000000000000000000000000000..aab5c5f9f38542c11cbc61c42518c939237b0779 --- /dev/null +++ b/frontend/src/api.js @@ -0,0 +1,182 @@ +// =============================================== +// 🔐 API Layer for Adaptive AI NIDS Frontend +// =============================================== + +const BASE_URL = + import.meta.env.VITE_API_URL || "http://127.0.0.1:5000"; + +// Safe fetch wrapper +async function safeFetch(url, options = {}, timeout = 10000, retries = 1) { + const controller = new AbortController(); + const id = setTimeout(() => controller.abort(), timeout); + + try { + const res = await fetch(url, { ...options, signal: controller.signal }); + + if (!res.ok) { + const errText = await res.text(); + throw new Error(`HTTP ${res.status}: ${errText}`); + } + return await res.json(); + } catch (err) { + console.error(`❌ API Error [${url}]:`, err.message); + + if (retries > 0 && url.includes("/geo/")) { + await new Promise((r) => setTimeout(r, 1500)); + return safeFetch(url, options, timeout * 1.5, retries - 1); + } + + return { error: err.message }; + } finally { + clearTimeout(id); + } +} + +// ------------------------------------------------------------- +// 🚀 LIVE CAPTURE +// ------------------------------------------------------------- +export async function startSniffer(iface = null) { + const q = iface ? `?iface=${iface}` : ""; + return safeFetch(`${BASE_URL}/api/live/start${q}`); +} + +export async function stopSniffer() { + return safeFetch(`${BASE_URL}/api/live/stop`); +} + +export async function getStatus() { + return safeFetch(`${BASE_URL}/api/live/status`); +} + +// MODEL-AWARE 🎯 +export async function getRecent(model, limit = 300) { + const res = await safeFetch(`${BASE_URL}/api/live/recent?model=${model}`); + + if (res?.events && Array.isArray(res.events)) { + res.events = res.events.slice(-limit); + } + return res; +} + +// MODEL-AWARE 🎯 +export async function getStats(model) { + return safeFetch(`${BASE_URL}/api/live/stats?model=${model}`); +} + +// ------------------------------------------------------------- +// 🧾 LOGS (MODEL-AWARE) +// ------------------------------------------------------------- +export function download_logs(model) { + window.location.href = `${BASE_URL}/api/logs/download?model=${model}`; +} + +export async function clearLogs(model, n = 50) { + return safeFetch( + `${BASE_URL}/api/logs/clear?model=${model}&n=${n}`, + { method: "POST" } + ); +} + +export async function clearByPrediction(model, pred) { + return safeFetch( + `${BASE_URL}/api/logs/clear_pred?model=${model}&pred=${pred}`, + { method: "POST" } + ); +} + +export async function deleteOne(model, index) { + return safeFetch( + `${BASE_URL}/api/logs/delete_one?model=${model}&index=${index}`, + { method: "POST" } + ); +} + +// ------------------------------------------------------------- +// 🌍 GEO + ALERTS +// ------------------------------------------------------------- +export async function getGeoData() { + return safeFetch(`${BASE_URL}/api/geo/recent`, {}, 20000, 2); +} + +export async function getAlerts() { + return safeFetch(`${BASE_URL}/api/alerts`); +} + +// ------------------------------------------------------------- +// 🧩 MODEL CONTROL +// ------------------------------------------------------------- +export async function getActiveModel() { + return safeFetch(`${BASE_URL}/api/model/active`); +} + +export async function switchModel(model) { + return safeFetch(`${BASE_URL}/api/model/select`, { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ model }), + }); +} + +export async function getModelHealth() { + return safeFetch(`${BASE_URL}/api/model/health`); +} + + +// ------------------------------------------------------------- +// 🤖 AI HELPERS +// ------------------------------------------------------------- +export async function explainThreat(event) { + return safeFetch(`${BASE_URL}/api/ai/explain`, { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify(event), + }); +} + +export async function getAISummary(model, n = 200) { + return safeFetch( + `${BASE_URL}/api/ai/summary?model=${encodeURIComponent(model)}&n=${n}` + ); +} + + +export async function sendMessageToAI(message) { + try { + const res = await fetch(`${BASE_URL}/api/chat`, { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ message }) + }); + + if (!res.ok) throw new Error("Chat API failed"); + return res.json(); + } catch (err) { + console.error("Chat error:", err); + return { reply: "⚠ AI Assistant not responding." }; + } +} + +// ➤ Offline CSV/PCAP prediction +export const offlinePredictAPI = async (file, model) => { + const formData = new FormData(); + formData.append("file", file); + formData.append("model", model); + + const res = await fetch("http://127.0.0.1:5000/api/offline/predict", { + method: "POST", + body: formData, + }); + + return res.json(); +}; + +// ➤ Get PDF forensic report download link +export const downloadOfflineReport = () => { + window.open("http://127.0.0.1:5000/api/offline/report", "_blank"); +}; + + +// ------------------------------------------------------------- +export { BASE_URL }; + + diff --git a/frontend/src/assests/Dos-HTTPtest.jpg b/frontend/src/assests/Dos-HTTPtest.jpg new file mode 100644 index 0000000000000000000000000000000000000000..d0ee11971cd2f071f4fb4d65dde7034359dc3ee2 Binary files /dev/null and b/frontend/src/assests/Dos-HTTPtest.jpg differ diff --git a/frontend/src/assests/Dos_Attacks-hulk.jpeg b/frontend/src/assests/Dos_Attacks-hulk.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..7a7d44c8d1a48d6348eff643a9c2db801a1387ef Binary files /dev/null and b/frontend/src/assests/Dos_Attacks-hulk.jpeg differ diff --git a/frontend/src/assests/HOIC.jpeg b/frontend/src/assests/HOIC.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..eaba03516551e68cc99795a2a398f5f95609c081 Binary files /dev/null and b/frontend/src/assests/HOIC.jpeg differ diff --git a/frontend/src/assests/LOic.jpeg b/frontend/src/assests/LOic.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..fcbcb8c390ad51d908664e3b3ef1261b3a3573d4 Binary files /dev/null and b/frontend/src/assests/LOic.jpeg differ diff --git a/frontend/src/assests/heroBlocks.svg b/frontend/src/assests/heroBlocks.svg new file mode 100644 index 0000000000000000000000000000000000000000..ba0c5004a6fb40218a499f24444f7a409629d17c --- /dev/null +++ b/frontend/src/assests/heroBlocks.svg @@ -0,0 +1,30 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/frontend/src/assests/sql.jpeg b/frontend/src/assests/sql.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..5601028bf89da07e2ef36973dad98f0d959e3fdd Binary files /dev/null and b/frontend/src/assests/sql.jpeg differ diff --git a/frontend/src/assests/vpn.jpg b/frontend/src/assests/vpn.jpg new file mode 100644 index 0000000000000000000000000000000000000000..e3cd42beffbd56ecb57427d7054ec990f8ca6869 Binary files /dev/null and b/frontend/src/assests/vpn.jpg differ diff --git a/frontend/src/assests/web.jpeg b/frontend/src/assests/web.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..e6de3cc124ae020f609e62bcdc039720ba56949e Binary files /dev/null and b/frontend/src/assests/web.jpeg differ diff --git a/frontend/src/components/Alerts.jsx b/frontend/src/components/Alerts.jsx new file mode 100644 index 0000000000000000000000000000000000000000..ade84bbaaf4222a43b8d98c2ba84b6945167e9f7 --- /dev/null +++ b/frontend/src/components/Alerts.jsx @@ -0,0 +1,36 @@ +import React, { useContext } from "react"; +import { AlertTriangle, ShieldAlert, Info } from "lucide-react"; +import { AlertContext } from "../context/AlertContext.jsx"; + +export default function Alerts() { + const { alerts, removeAlert } = useContext(AlertContext); + + const COLORS = { + info: "border-blue-400/40 bg-blue-500/10 text-blue-300", + warn: "border-yellow-400/40 bg-yellow-500/10 text-yellow-300", + danger: "border-red-400/40 bg-red-500/10 text-red-300", + }; + + const ICONS = { + info: , + warn: , + danger: , + }; + + return ( +
+ {alerts.map(a => ( +
removeAlert(a.id)} + > + {ICONS[a.type]} + {a.msg} +
+ ))} +
+ ); +} diff --git a/frontend/src/components/ConstellationBackground.jsx b/frontend/src/components/ConstellationBackground.jsx new file mode 100644 index 0000000000000000000000000000000000000000..5bb21252c1990f50a0e39ee343f3fe0f90d36b48 --- /dev/null +++ b/frontend/src/components/ConstellationBackground.jsx @@ -0,0 +1,85 @@ +import { useEffect, useRef } from "react"; + +export default function ConstellationBackground() { + const canvasRef = useRef(null); + + useEffect(() => { + const canvas = canvasRef.current; + const ctx = canvas.getContext("2d"); + + let particles = []; + const count = 140; // number of stars + + // resize canvas + function resize() { + canvas.width = window.innerWidth; + canvas.height = window.innerHeight; + } + resize(); + window.addEventListener("resize", resize); + + // create particles + for (let i = 0; i < count; i++) { + particles.push({ + x: Math.random() * canvas.width, + y: Math.random() * canvas.height, + vx: (Math.random() - 0.5) * 0.3, + vy: (Math.random() - 0.5) * 0.3, + size: Math.random() * 2 + 1, + }); + } + + // animation loop + function animate() { + ctx.clearRect(0, 0, canvas.width, canvas.height); + + // draw particles + particles.forEach((p) => { + p.x += p.vx; + p.y += p.vy; + + // soft bounce + if (p.x < 0 || p.x > canvas.width) p.vx *= -1; + if (p.y < 0 || p.y > canvas.height) p.vy *= -1; + + ctx.beginPath(); + ctx.arc(p.x, p.y, p.size, 0, Math.PI * 2); + ctx.fillStyle = "rgba(0, 220, 255, 0.9)"; + ctx.shadowBlur = 10; + ctx.shadowColor = "#66e0ff"; + ctx.fill(); + }); + + // draw connecting lines + for (let i = 0; i < count; i++) { + for (let j = i + 1; j < count; j++) { + const a = particles[i]; + const b = particles[j]; + const dist = Math.hypot(a.x - b.x, a.y - b.y); + + if (dist < 140) { + ctx.strokeStyle = `rgba(120, 255, 255, ${1 - dist / 140})`; + ctx.lineWidth = 0.7; + ctx.beginPath(); + ctx.moveTo(a.x, a.y); + ctx.lineTo(b.x, b.y); + ctx.stroke(); + } + } + } + + requestAnimationFrame(animate); + } + + animate(); + + return () => window.removeEventListener("resize", resize); + }, []); + + return ( + + ); +} diff --git a/frontend/src/components/Header.jsx b/frontend/src/components/Header.jsx new file mode 100644 index 0000000000000000000000000000000000000000..8378b063e8499f9b5910040856d0ba4d18197251 --- /dev/null +++ b/frontend/src/components/Header.jsx @@ -0,0 +1,16 @@ +// src/components/Header.jsx +import React from "react"; +import { useAuth } from "../context/AuthContext"; + +export default function Header() { + const { user, logout } = useAuth(); + return ( +
+
NIDS Cyber Defense
+
+
{user?.email}
+ +
+
+ ); +} diff --git a/frontend/src/components/IPInfoModal.jsx b/frontend/src/components/IPInfoModal.jsx new file mode 100644 index 0000000000000000000000000000000000000000..014eb49da4c1d8662afcaa22c39b7bb4fe89ff0e --- /dev/null +++ b/frontend/src/components/IPInfoModal.jsx @@ -0,0 +1,182 @@ +import React, { useEffect, useState } from "react"; + +export default function IPInfoModal({ ip, onClose }) { + const [info, setInfo] = useState(null); + const [loading, setLoading] = useState(true); + const [risk, setRisk] = useState({ + level: "Unknown", + score: 0, + reason: "Unverified", + }); + + useEffect(() => { + if (!ip) return; + + // Reset state for new IP + setInfo(null); + setRisk({ level: "Unknown", score: 0, reason: "Unverified" }); + setLoading(true); + + // ✅ Private / Local IP detection + const privateRanges = [ + /^127\./, + /^10\./, + /^192\.168\./, + /^172\.(1[6-9]|2[0-9]|3[0-1])\./, + /^239\./, // multicast + ]; + + if (privateRanges.some((r) => r.test(ip))) { + setInfo({ + city: "Local Network", + region: "Private or Multicast Range", + country_name: "N/A", + org: "Local Device", + asn: "LAN", + version: "IPv4", + latitude: "-", + longitude: "-", + }); + setRisk({ + level: "Low", + score: 5, + reason: "Private / local or multicast IP", + }); + setLoading(false); + return; + } + + // ✅ Backend lookup (new route) +fetch(`http://127.0.0.1:5000/api/geo/resolve?ip=${ip}`) + .then(async (res) => { + if (!res.ok) { + const errData = await res.json().catch(() => ({})); + throw new Error(errData.error || "Lookup failed"); + } + return res.json(); + }) + .then((data) => { + setInfo(data); + setRisk({ + level: + data.country === "Local" + ? "Low" + : data.country === "Unknown" + ? "Medium" + : "Low", + score: data.country === "Unknown" ? 40 : 15, + reason: `Detected in ${data.country || "Unknown"}`, + }); + }) + .catch((err) => { + console.warn("⚠️ Geo lookup failed:", err.message); + setInfo({ + city: "Unknown", + country_name: "Unknown", + org: "No data", + latitude: "-", + longitude: "-", + }); + setRisk({ + level: "Low", + score: 10, + reason: "No threat detected / lookup failed", + }); + }) + .finally(() => setLoading(false)); + }, [ip]); + + if (!ip) return null; + + // ✅ Helper for dynamic color + const riskColor = + risk.level === "High" + ? "text-red-400" + : risk.level === "Medium" + ? "text-yellow-400" + : "text-green-400"; + + const riskBarColor = + risk.level === "High" + ? "bg-red-500" + : risk.level === "Medium" + ? "bg-yellow-400" + : "bg-green-400"; + + return ( +
+
+

+ 🌐 IP Info — {ip} +

+ + {loading ? ( +

+ Fetching IP intelligence... +

+ ) : info ? ( +
+

+ City: {info.city || "Unknown"} +

+

+ Region: {info.region || "Unknown"} +

+

+ Country: {info.country_name || "Unknown"} +

+

+ ISP: {info.org || "Unknown"} +

+

+ ASN: {info.asn || "N/A"} +

+

+ IP Type: {info.version} +

+

+ Coordinates: {info.latitude}, {info.longitude} +

+
+ ) : ( +

+ Could not fetch IP intelligence data. +

+ )} + + {/* Risk / Threat assessment */} + {!loading && ( +
+

Threat Assessment

+
+ + {risk.level} Risk ({risk.score}%) + + + {risk.reason} + +
+ + {/* Risk Progress Bar */} +
+
+
+
+ )} + + {/* Close button */} + +
+
+ ); +} + + diff --git a/frontend/src/components/LoadingOverlay.jsx b/frontend/src/components/LoadingOverlay.jsx new file mode 100644 index 0000000000000000000000000000000000000000..5005452cd1ee0e85f6154e595f9846d91fc135eb --- /dev/null +++ b/frontend/src/components/LoadingOverlay.jsx @@ -0,0 +1,12 @@ +// src/components/LoadingOverlay.jsx +export default function LoadingOverlay({ show, message = "Processing..." }) { + if (!show) return null; + return ( +
+
+
+

{message}

+
+
+ ); +} diff --git a/frontend/src/components/MainLayout.jsx b/frontend/src/components/MainLayout.jsx new file mode 100644 index 0000000000000000000000000000000000000000..79f618f1eee1e4fa7a794710560cdca7c0fba926 --- /dev/null +++ b/frontend/src/components/MainLayout.jsx @@ -0,0 +1,13 @@ +import React from "react"; +import Navbar from "./Navbar"; +import ChatAssistant from "../pages/ChatAssistant"; + +export default function MainLayout({ children }) { + return ( +
+ + {children} + +
+ ); +} diff --git a/frontend/src/components/Navbar.jsx b/frontend/src/components/Navbar.jsx new file mode 100644 index 0000000000000000000000000000000000000000..f70700ecc928333ce869cfa85d90b86311633635 --- /dev/null +++ b/frontend/src/components/Navbar.jsx @@ -0,0 +1,128 @@ +import React, { useState } from "react"; +import { Shield, Search, User, ChevronDown, RefreshCcw, LogOut, CurrencyIcon } from "lucide-react"; +import { useAuth } from "../context/AuthContext"; +import { useLocation, useNavigate } from "react-router-dom"; + +export default function Navbar({ onRefresh }) { + const { user, logout } = useAuth(); + const navigate = useNavigate(); + const location = useLocation(); + + const [searchQuery, setSearchQuery] = useState(""); + const [profileOpen, setProfileOpen] = useState(false); + + return ( + + ); +} diff --git a/frontend/src/components/NeonShield.jsx b/frontend/src/components/NeonShield.jsx new file mode 100644 index 0000000000000000000000000000000000000000..9ca4820ba3084c0688dda5ea62f53c39cd06ab6c --- /dev/null +++ b/frontend/src/components/NeonShield.jsx @@ -0,0 +1,87 @@ +import { Canvas, useFrame } from "@react-three/fiber"; +import * as THREE from "three"; +import { useRef } from "react"; + +/* ------------- Rotating Packet Ring ------------- */ +function PacketRing() { + const ring = useRef(); + const pulse = useRef(); + + useFrame(({ clock }) => { + const t = clock.getElapsedTime(); + + ring.current.rotation.y = t * 0.6; + + const r = 1.5; + pulse.current.position.x = r * Math.cos(t * 1.5); + pulse.current.position.y = r * Math.sin(t * 1.5); + }); + + return ( + + + + + + + + + + + + ); +} + +/* ------------- Floating Network Nodes ------------- */ +function NetworkNodes() { + const group = useRef(); + + useFrame(({ clock }) => { + group.current.rotation.y = clock.getElapsedTime() * 0.3; + }); + + const nodes = Array.from({ length: 12 }).map(() => [ + (Math.random() - 0.5) * 2, + (Math.random() - 0.5) * 1.5, + (Math.random() - 0.5) * 1.2, + ]); + + return ( + + {nodes.map((pos, i) => ( + + + + + ))} + + ); +} + +/* ------------- Main Scene ------------- */ +export default function NIDS3D() { + return ( + + + + + + + + + + ); +} + + diff --git a/frontend/src/components/ProtectedRoute.jsx b/frontend/src/components/ProtectedRoute.jsx new file mode 100644 index 0000000000000000000000000000000000000000..f83c96446e12d3217180f0afb3089923a7bb94d0 --- /dev/null +++ b/frontend/src/components/ProtectedRoute.jsx @@ -0,0 +1,10 @@ +// src/components/ProtectedRoute.jsx +import React from "react"; +import { Navigate } from "react-router-dom"; +import { useAuth } from "../context/AuthContext"; + +export default function ProtectedRoute({ children }) { + const { user, authLoading } = useAuth(); + if (authLoading) return null; // or a spinner + return user ? children : ; +} diff --git a/frontend/src/components/Sidebar.jsx b/frontend/src/components/Sidebar.jsx new file mode 100644 index 0000000000000000000000000000000000000000..5212e8af6f8ecfcb3dfb8d1fe69413d0302fef15 --- /dev/null +++ b/frontend/src/components/Sidebar.jsx @@ -0,0 +1,324 @@ +// src/components/Sidebar.jsx +import React, { useEffect, useRef, useState } from "react"; +import { NavLink } from "react-router-dom"; +import { + Activity, + AlertTriangle, + FileText, + Info, + Settings, + TrafficCone, + MonitorCog, + GitBranchMinus, + BrickWallShield, + ChevronLeft, + ChevronRight, + MoonStar, + LayoutDashboard, +} from "lucide-react"; + +/** + * Sidebar with mini-constellation canvas (B3) + * + * Props: + * - collapsed: boolean + * - setCollapsed: function + */ +export default function Sidebar({ collapsed, setCollapsed }) { + const sidebarRef = useRef(null); + const canvasRef = useRef(null); + const [time, setTime] = useState(""); + + // clock tick + useEffect(() => { + const tick = () => { + const now = new Date(); + setTime( + now.toLocaleTimeString("en-IN", { + hour12: true, + hour: "2-digit", + minute: "2-digit", + second: "2-digit", + }) + ); + }; + tick(); + const id = setInterval(tick, 1000); + return () => clearInterval(id); + }, []); + + // mini-constellation canvas (node+line) - optimized for sidebar + useEffect(() => { + const canvas = canvasRef.current; + const sidebar = sidebarRef.current; + if (!canvas || !sidebar) return; + + const ctx = canvas.getContext("2d", { alpha: true }); + let width = sidebar.clientWidth; + let height = sidebar.clientHeight; + let rafId = null; + let particles = []; + const COUNT = Math.max(18, Math.floor((width * height) / 10000)); // scale nodes by size + + // Resize handler + function resize() { + width = sidebar.clientWidth; + height = sidebar.clientHeight; + const dpr = Math.max(1, window.devicePixelRatio || 1); + canvas.width = Math.floor(width * dpr); + canvas.height = Math.floor(height * dpr); + canvas.style.width = `${width}px`; + canvas.style.height = `${height}px`; + ctx.setTransform(dpr, 0, 0, dpr, 0, 0); + // recreate particles to adapt density + initParticles(); + } + + function rand(min, max) { + return Math.random() * (max - min) + min; + } + + function initParticles() { + particles = []; + const n = COUNT; + for (let i = 0; i < n; i++) { + particles.push({ + x: rand(10, width - 10), + y: rand(10, height - 10), + vx: rand(-0.15, 0.15), + vy: rand(-0.15, 0.15), + size: rand(0.8, 2.2), + hue: rand(170, 200), // cyan-ish + }); + } + } + + function step() { + ctx.clearRect(0, 0, width, height); + + // draw lines between close particles + for (let i = 0; i < particles.length; i++) { + const a = particles[i]; + for (let j = i + 1; j < particles.length; j++) { + const b = particles[j]; + const dx = a.x - b.x; + const dy = a.y - b.y; + const dist = Math.sqrt(dx * dx + dy * dy); + const MAX = Math.min(120, Math.max(70, (width + height) / 15)); + if (dist < MAX) { + const alpha = 0.12 * (1 - dist / MAX); + ctx.beginPath(); + ctx.moveTo(a.x, a.y); + ctx.lineTo(b.x, b.y); + ctx.strokeStyle = `rgba(40,220,210, ${alpha.toFixed(3)})`; + ctx.lineWidth = 0.6; + ctx.stroke(); + } + } + } + + // draw nodes + for (let p of particles) { + // move + p.x += p.vx; + p.y += p.vy; + + // gentle wrap/bounce + if (p.x < -6) p.x = width + 6; + if (p.x > width + 6) p.x = -6; + if (p.y < -6) p.y = height + 6; + if (p.y > height + 6) p.y = -6; + + // glow circle + ctx.beginPath(); + const grad = ctx.createRadialGradient(p.x, p.y, 0, p.x, p.y, p.size * 6); + grad.addColorStop(0, `rgba(0,230,255,0.95)`); + grad.addColorStop(0.2, `rgba(0,230,255,0.55)`); + grad.addColorStop(1, `rgba(0,230,255,0.02)`); + ctx.fillStyle = grad; + ctx.arc(p.x, p.y, p.size * 3.2, 0, Math.PI * 2); + ctx.fill(); + + // center bright dot + ctx.beginPath(); + ctx.fillStyle = `rgba(220,255,255,0.95)`; + ctx.arc(p.x, p.y, p.size, 0, Math.PI * 2); + ctx.fill(); + } + + rafId = requestAnimationFrame(step); + } + + // ResizeObserver to respond to sidebar size changes (collapse/expand) + const ro = new ResizeObserver(resize); + ro.observe(sidebar); + + resize(); + step(); // start animation + + return () => { + if (rafId) cancelAnimationFrame(rafId); + ro.disconnect(); + }; + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [sidebarRef, canvasRef, collapsed]); + + // navigation items + const navItems = [ + { to: "/", label: "Dashboard", icon: }, + { to: "/livetraffic", label: "Live Traffic", icon: }, + { to: "/threats", label: "Threat Intelligence", icon: }, + { to: "/alerts", label: "Alerts", icon: }, + { to: "/incidents", label: "Offline Detection", icon: }, + { to: "/traffic", label: "Traffic Analysis", icon: }, + { to: "/response", label: "Response System", icon: }, + { to: "/mlmodels", label: "ML Model", icon: }, + { to: "/reports", label: "Reports", icon: }, + { to: "/system", label: "System Info", icon: }, + { to: "/settings", label: "Settings", icon: }, + ]; + + return ( + + ); +} + diff --git a/frontend/src/components/TopStatusBar.jsx b/frontend/src/components/TopStatusBar.jsx new file mode 100644 index 0000000000000000000000000000000000000000..45d42462649ff768bb2795f49878b9a52fe1942e --- /dev/null +++ b/frontend/src/components/TopStatusBar.jsx @@ -0,0 +1,13 @@ +// src/components/TopStatusBar.jsx +export default function TopStatusBar({ running, packetCount, iface, lastUpdate }) { + return ( +
+ + {running ? "🟢 Capturing Packets" : "🔴 Capture Stopped"} + + + Packets: {packetCount} | Interface: {iface || "auto"} | Updated: {lastUpdate || "—"} + +
+ ); +} diff --git a/frontend/src/components/dashboard/Controls.jsx b/frontend/src/components/dashboard/Controls.jsx new file mode 100644 index 0000000000000000000000000000000000000000..ef74ec023a81f0320545f7c050da1f39cd625e38 --- /dev/null +++ b/frontend/src/components/dashboard/Controls.jsx @@ -0,0 +1,55 @@ +import React, { useContext, useState, useEffect } from "react"; +import { LiveDataContext } from "../../context/DataContext"; +import { startSniffer, stopSniffer, getStatus } from "../../api"; + +export default function Controls() { + const { running, setRunning } = useContext(LiveDataContext); + const [loading, setLoading] = useState(false); + + useEffect(() => { + (async () => { + const res = await getStatus(); + setRunning(res?.running || false); + })(); + }, []); + + const handleStart = async () => { + setLoading(true); + // 🔥 RESET BUFFER on START + window.__EVENT_BUFFER_RESET = true; + + await startSniffer(); + setRunning(true); + setLoading(false); + }; + + const handleStop = async () => { + setLoading(true); + await stopSniffer(); + setRunning(false); + setLoading(false); + }; + + return ( +
+ + +
+ ); +} diff --git a/frontend/src/components/dashboard/LiveDashboard.jsx b/frontend/src/components/dashboard/LiveDashboard.jsx new file mode 100644 index 0000000000000000000000000000000000000000..7e80917f35292ca4af8989d65e4f55df991bd23c --- /dev/null +++ b/frontend/src/components/dashboard/LiveDashboard.jsx @@ -0,0 +1,254 @@ +import React, { useEffect, useState, useContext, useRef } from "react"; +import Controls from "./Controls.jsx"; +import LiveTable from "./LiveTable.jsx"; +import ChatAssistant from "../../pages/ChatAssistant.jsx"; +import StatsPanel from "./StatsPanel.jsx"; +import { Download, Loader2, MessageSquare } from "lucide-react"; +import { socket } from "../../socket.js"; +import { + getRecent, + getStats, + download_logs, + switchModel, + getActiveModel, + getStatus +} from "../../api.js"; +import { AlertContext } from "../../context/AlertContext.jsx"; +import ThreatFeed from "./ThreatFeed"; +import ThreatTimeline from "./ThreatTimeline"; +import TopIPs from "./TopIPs"; +import TopCountries from "./TopCountries"; +import Sparkline from "./Sparkline"; + +export default function LiveDashboard() { + const [rows, setRows] = useState([]); + const [stats, setStats] = useState({}); + const [connected, setConnected] = useState(false); + const [lastUpdate, setLastUpdate] = useState(null); + const [threatCount, setThreatCount] = useState(0); + const { pushAlert } = useContext(AlertContext); + + const [model, setModel] = useState("bcc"); + const [snifferRunning, setSnifferRunning] = useState(false); + const [switching, setSwitching] = useState(false); + const [modelReady, setModelReady] = useState(false); + + const [tableKey, setTableKey] = useState(0); + const eventBufferRef = useRef([]); + + // ============================================================ + // INIT LOAD: Active model + sniffer status + // ============================================================ + useEffect(() => { + (async () => { + const res = await getActiveModel(); + if (res?.model) setModel(res.model); + + const st = await getStatus(); + setSnifferRunning(st?.running === true); + })(); + }, []); + + // ============================================================ + // MODEL SWITCH (backend + UI reset) + // ============================================================ + useEffect(() => { + const doSwitch = async () => { + setSwitching(true); + setModelReady(false); + + setRows([]); + setStats({}); + setThreatCount(0); + eventBufferRef.current = []; + setTableKey((k) => k + 1); + + const res = await switchModel(model); + + if (!res?.error) { + pushAlert(`${model.toUpperCase()} model activated`, "info"); + setModelReady(true); + } else { + pushAlert("Model switch failed!", "danger"); + } + + setSwitching(false); + }; + + doSwitch(); + }, [model]); + + // ============================================================ + // INITIAL FETCH (after model switch) + // ============================================================ + useEffect(() => { + if (!modelReady) return; + (async () => { + const [r1, r2] = await Promise.all([ + getRecent(model), + getStats(model) + ]); + + setRows(Array.isArray(r1?.events) ? r1.events : []); + setStats(r2 || {}); + })(); + }, [model, modelReady]); + + // ============================================================ + // SOCKET LIVE EVENTS + // ============================================================ + useEffect(() => { + socket.on("connect", () => setConnected(true)); + socket.on("disconnect", () => setConnected(false)); + + socket.on("new_event", (payload) => { + if (!modelReady || switching) return; + const events = payload?.items ?? []; + if (events.length) eventBufferRef.current.push(...events); + }); + + return () => { + socket.off("connect"); + socket.off("disconnect"); + socket.off("new_event"); + }; + }, [modelReady, switching]); + + // ============================================================ + // BUFFER TO UI processing + // ============================================================ + useEffect(() => { + const interval = setInterval(() => { + if (!modelReady) return; + if (!eventBufferRef.current.length) return; + + const batch = eventBufferRef.current.splice(0, 50); + + setRows((prev) => [...batch, ...prev].slice(0, 180)); + + setStats((prev) => { + const next = { ...prev }; + batch.forEach((evt) => { + const label = (evt.prediction || "UNKNOWN").toUpperCase(); + next[label] = (next[label] || 0) + 1; + }); + return next; + }); + + batch.forEach((evt) => { + const label = (evt.prediction || "").toUpperCase(); + const isThreat = (model === "bcc" && ["TOR","I2P","ZERONET"].includes(label)) || + (model === "cicids" && label !== "BENIGN"); + if (isThreat) setThreatCount((c) => c + 1); + }); + + setLastUpdate(new Date().toLocaleTimeString()); + }, 1200); + + return () => clearInterval(interval); + }, [model, modelReady]); + + // ============================================================ + // AI Assistant Handler (local popup) + // ============================================================ + const askAISummary = async () => { + const dangerous = rows.filter(evt => { + const label = (evt.prediction || "").toUpperCase(); + return (model === "bcc" && ["TOR","I2P","ZERONET"].includes(label)) || + (model === "cicids" && label !== "BENIGN"); + }); + + alert(`AI Summary: +Danger Events: ${dangerous.length} +Recent Threat Types: ${dangerous.slice(0, 5).map(e => e.prediction).join(", ")}`); + }; + + // ============================================================ + // RENDER UI + // ============================================================ + return ( +
+

+ Live Capture +

+ +
+ Active Model: {model.toUpperCase()} +
+ + {/* Model Toggle */} +
+ + + + + {/* 🧠 AI Button */} + +
+ + + + {/* Status + Logs */} +
+

+ {model === "bcc" ? "Realtime BCC Monitor" : "Realtime CICIDS Monitor"} +

+ + +
+ + + + + + + + + {/* VISUAL ANALYTICS BACK ADDED */} +
+ + + + + +
+ +

+ Last packet: {lastUpdate || "Waiting…"} +

+
+ ); +} + + diff --git a/frontend/src/components/dashboard/LiveTable.jsx b/frontend/src/components/dashboard/LiveTable.jsx new file mode 100644 index 0000000000000000000000000000000000000000..ca914b00e01b55985c7ef1a1b0110b4800c4686a --- /dev/null +++ b/frontend/src/components/dashboard/LiveTable.jsx @@ -0,0 +1,168 @@ +import React, { useState } from "react"; +import Badge from "../../ui/Badge"; +import { clearLogs, clearByPrediction, deleteOne } from "../../api"; +import { Trash } from "lucide-react"; +import IPInfoModal from "../IPInfoModal"; + +export default function LiveTable({ rows, refresh }) { + const [clearing, setClearing] = useState(false); + const [deleteCount, setDeleteCount] = useState(50); + const [selectedIP, setSelectedIP] = useState(null); + + + + // ✅ Safely clear last N rows + const handleClear = async (n) => { + try { + setClearing(true); + // 🔥 RESET BUFFER on CLEAR + window.__EVENT_BUFFER_RESET = true; + await clearLogs(n); + if (typeof refresh === "function") await refresh(); + } catch (err) { + console.error("Clear logs failed:", err); + } finally { + setTimeout(() => setClearing(false), 500); + } + }; + + // ✅ Clear logs by prediction type + const handleClearPrediction = async (pred) => { + if (!pred) return; + try { + await clearByPrediction(pred); + if (typeof refresh === "function") await refresh(); + } catch (err) { + console.error("Clear by prediction failed:", err); + } + }; + + // ✅ Delete a single row safely + const deleteSingleRow = async (index) => { + try { + await deleteOne(index); + if (typeof refresh === "function") await refresh(); + } catch (err) { + console.error("Delete single row failed:", err); + } + }; + + return ( +
+ {/* HEADER + ACTIONS */} +
+
+

Live Events

+ + Showing last {rows.length} packets + +
+ + {/* DELETE CONTROLS */} +
+ {/* DELETE BY PREDICTION */} + + + {/* CLEAR LAST N */} + + + {/* CLEAR ALL */} + +
+
+ + {/* TABLE */} +
+ + + + + + + + + + + + + {rows + .slice() + .reverse() + .map((r, idx) => ( + + + + + + + + ))} + +
TimeSrc → DstPortsProtoPrediction
{r.time} + setSelectedIP(r.src_ip || r.src)} + > + {r.src_ip || r.src} + {" "} + → + setSelectedIP(r.dst_ip || r.dst)} + > + {r.dst_ip || r.dst} + + + {r.sport} → {r.dport} + {r.proto} + + + deleteSingleRow(rows.length - 1 - idx) + } + /> +
+
+ + {/* ✅ IP Lookup Modal */} + {selectedIP && ( + setSelectedIP(null)} + /> + )} +
+ ); +} diff --git a/frontend/src/components/dashboard/Sparkline.jsx b/frontend/src/components/dashboard/Sparkline.jsx new file mode 100644 index 0000000000000000000000000000000000000000..83cc0fcaff1e1b08f68b2976ff252e079cba1720 --- /dev/null +++ b/frontend/src/components/dashboard/Sparkline.jsx @@ -0,0 +1,28 @@ +import { LineChart, Line, ResponsiveContainer } from "recharts"; + +export default function Sparkline({ events }) { + const data = events.slice(-40).map((e, i) => ({ + index: i, + value: (e.prediction || "").toUpperCase() !== "UNKNOWN" ? 1 : 0, + })); + + return ( +
+

Threat Activity Trend

+ +
+ + + + + +
+
+ ); +} diff --git a/frontend/src/components/dashboard/StatsPanel.jsx b/frontend/src/components/dashboard/StatsPanel.jsx new file mode 100644 index 0000000000000000000000000000000000000000..7e131237d4377224da92742da8f32bb0774ebb4e --- /dev/null +++ b/frontend/src/components/dashboard/StatsPanel.jsx @@ -0,0 +1,104 @@ +import React, { useMemo } from "react"; +import { + PieChart, + Pie, + Cell, + Tooltip, + ResponsiveContainer, + BarChart, + Bar, + XAxis, + YAxis, + CartesianGrid, +} from "recharts"; + +// 🎨 Neon cyber color palette +const COLORS = ["#00e5ff", "#ff0059", "#a78bfa", "#fbbf24", "#10b981"]; + +export default function StatsPanel({ stats }) { + // ✅ Memoized data transformation for performance + const data = useMemo(() => { + if (!stats || typeof stats !== "object") return []; + return Object.entries(stats) + .map(([name, value]) => ({ + name: String(name).toUpperCase(), + value: Number(value) || 0, + })) + .filter((d) => d.value > 0); + }, [stats]); + + const total = data.reduce((sum, d) => sum + d.value, 0); + + return ( +
+ {/* ==== PIE CHART ==== */} +
+

+ Class Distribution (Pie) +

+ +
+ {data.length > 0 ? ( + + + `${name}: ${value}`} + > + {data.map((entry, index) => ( + + ))} + + + + + ) : ( +
+ No Data Yet +
+ )} +
+ + {/* Total Packets Indicator */} +

+ Total Packets:{" "} + {total} +

+
+ + {/* ==== BAR CHART ==== */} +
+

+ Class Counts (Bar) +

+ +
+ {data.length > 0 ? ( + + + + + + + + + + ) : ( +
+ Awaiting Data... +
+ )} +
+
+
+ ); +} + diff --git a/frontend/src/components/dashboard/ThreatFeed.jsx b/frontend/src/components/dashboard/ThreatFeed.jsx new file mode 100644 index 0000000000000000000000000000000000000000..7e7c23d80b8b4494d9c943798d55830e3e7f0a36 --- /dev/null +++ b/frontend/src/components/dashboard/ThreatFeed.jsx @@ -0,0 +1,39 @@ +import { AlertTriangle } from "lucide-react"; + +export default function ThreatFeed({ events }) { + const highRisk = events + .filter((e) => + ["TOR", "I2P", "ZERONET"].includes((e.prediction || "").toUpperCase()) + ) + .slice(-20) + .reverse(); + + return ( +
+

+ High-Risk Events +

+ + {/* Scrollable list */} +
+ {highRisk.length === 0 ? ( +

No critical activity detected

+ ) : ( + highRisk.map((e, i) => ( +
+ {e.prediction} + from + {e.src_ip} +
+ )) + )} +
+
+ ); +} + diff --git a/frontend/src/components/dashboard/ThreatTimeline.jsx b/frontend/src/components/dashboard/ThreatTimeline.jsx new file mode 100644 index 0000000000000000000000000000000000000000..e182c3d16d6f9845e351f727e6e176c5863c3585 --- /dev/null +++ b/frontend/src/components/dashboard/ThreatTimeline.jsx @@ -0,0 +1,27 @@ +export default function ThreatTimeline({ events }) { + const last20 = [...events].slice(-20).reverse(); + + return ( +
+

Event Timeline

+ + {/* Scrollable container */} +
+ {last20.map((e, i) => ( +
+
+
+

+ {e.prediction}{" "} + detected from {e.src_ip} +

+

+ {new Date().toLocaleTimeString()} +

+
+
+ ))} +
+
+ ); +} diff --git a/frontend/src/components/dashboard/TopCountries.jsx b/frontend/src/components/dashboard/TopCountries.jsx new file mode 100644 index 0000000000000000000000000000000000000000..2546920996d9e808a1362b7084324420e2d66d08 --- /dev/null +++ b/frontend/src/components/dashboard/TopCountries.jsx @@ -0,0 +1,26 @@ +export default function TopCountries({ events }) { + const map = {}; + + events.forEach((e) => { + const c = e.src_country || "Unknown"; + map[c] = (map[c] || 0) + 1; + }); + + const sorted = Object.entries(map) + .sort((a, b) => b[1] - a[1]) + .slice(0, 6); + + return ( +
+

Top Source Countries

+
    + {sorted.map(([country, count], i) => ( +
  • + {country} + {count} +
    • + ))} +
+
+ ); +} diff --git a/frontend/src/components/dashboard/TopIPs.jsx b/frontend/src/components/dashboard/TopIPs.jsx new file mode 100644 index 0000000000000000000000000000000000000000..6b8a11d24d83120a1c4b32ebfb281c1314d3b4dd --- /dev/null +++ b/frontend/src/components/dashboard/TopIPs.jsx @@ -0,0 +1,26 @@ +export default function TopIPs({ events }) { + const map = {}; + + events.forEach((e) => { + const ip = e.src_ip || "Unknown"; + map[ip] = (map[ip] || 0) + 1; + }); + + const sorted = Object.entries(map) + .sort((a, b) => b[1] - a[1]) + .slice(0, 6); + + return ( +
+

Top Source IPs

+
    + {sorted.map(([ip, count], i) => ( +
  • + {ip} + {count} +
    • + ))} +
+
+ ); +} diff --git a/frontend/src/components/ui/accordion.jsx b/frontend/src/components/ui/accordion.jsx new file mode 100644 index 0000000000000000000000000000000000000000..a6bf4f3f8df50625e42cb7b132e06dd6504b7727 --- /dev/null +++ b/frontend/src/components/ui/accordion.jsx @@ -0,0 +1,43 @@ +"use client" + +import * as React from "react" +import * as AccordionPrimitive from "@radix-ui/react-accordion" +import { ChevronDown } from "lucide-react" + +import { cn } from "@/lib/utils" + +const Accordion = AccordionPrimitive.Root + +const AccordionItem = React.forwardRef(({ className, ...props }, ref) => ( + +)) +AccordionItem.displayName = "AccordionItem" + +const AccordionTrigger = React.forwardRef(({ className, children, ...props }, ref) => ( + + svg]:rotate-180", + className + )} + {...props}> + {children} + + + +)) +AccordionTrigger.displayName = AccordionPrimitive.Trigger.displayName + +const AccordionContent = React.forwardRef(({ className, children, ...props }, ref) => ( + +
{children}
+ +)) + +AccordionContent.displayName = AccordionPrimitive.Content.displayName + +export { Accordion, AccordionItem, AccordionTrigger, AccordionContent } diff --git a/frontend/src/components/ui/badge.jsx b/frontend/src/components/ui/badge.jsx new file mode 100644 index 0000000000000000000000000000000000000000..deae51c4a2f714ccbc27a14d6c8b2f510f59bb15 --- /dev/null +++ b/frontend/src/components/ui/badge.jsx @@ -0,0 +1,34 @@ +import * as React from "react" +import { cva } from "class-variance-authority"; + +import { cn } from "@/lib/utils" + +const badgeVariants = cva( + "inline-flex items-center rounded-full border px-2.5 py-0.5 text-xs font-semibold transition-colors focus:outline-none focus:ring-2 focus:ring-ring focus:ring-offset-2", + { + variants: { + variant: { + default: + "border-transparent bg-primary text-primary-foreground hover:bg-primary/80", + secondary: + "border-transparent bg-secondary text-secondary-foreground hover:bg-secondary/80", + destructive: + "border-transparent bg-destructive text-destructive-foreground hover:bg-destructive/80", + outline: "text-foreground", + }, + }, + defaultVariants: { + variant: "default", + }, + } +) + +function Badge({ + className, + variant, + ...props +}) { + return (
); +} + +export { Badge, badgeVariants } diff --git a/frontend/src/components/ui/button.jsx b/frontend/src/components/ui/button.jsx new file mode 100644 index 0000000000000000000000000000000000000000..ce4184bf8f1367d9aca4c07d9e638043ee50ce0a --- /dev/null +++ b/frontend/src/components/ui/button.jsx @@ -0,0 +1,47 @@ +import * as React from "react" +import { Slot } from "@radix-ui/react-slot" +import { cva } from "class-variance-authority"; + +import { cn } from "@/lib/utils" + +const buttonVariants = cva( + "inline-flex items-center justify-center gap-2 whitespace-nowrap rounded-md text-sm font-medium ring-offset-background transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:pointer-events-none disabled:opacity-50 [&_svg]:pointer-events-none [&_svg]:size-4 [&_svg]:shrink-0", + { + variants: { + variant: { + default: "bg-primary text-primary-foreground hover:bg-primary/90", + destructive: + "bg-destructive text-destructive-foreground hover:bg-destructive/90", + outline: + "border border-input bg-background hover:bg-accent hover:text-accent-foreground", + secondary: + "bg-secondary text-secondary-foreground hover:bg-secondary/80", + ghost: "hover:bg-accent hover:text-accent-foreground", + link: "text-primary underline-offset-4 hover:underline", + }, + size: { + default: "h-10 px-4 py-2", + sm: "h-9 rounded-md px-3", + lg: "h-11 rounded-md px-8", + icon: "h-10 w-10", + }, + }, + defaultVariants: { + variant: "default", + size: "default", + }, + } +) + +const Button = React.forwardRef(({ className, variant, size, asChild = false, ...props }, ref) => { + const Comp = asChild ? Slot : "button" + return ( + + ); +}) +Button.displayName = "Button" + +export { Button, buttonVariants } diff --git a/frontend/src/components/ui/card.jsx b/frontend/src/components/ui/card.jsx new file mode 100644 index 0000000000000000000000000000000000000000..c0ede2efd087447ac0604cf82ba061f46e69012a --- /dev/null +++ b/frontend/src/components/ui/card.jsx @@ -0,0 +1,50 @@ +import * as React from "react" + +import { cn } from "@/lib/utils" + +const Card = React.forwardRef(({ className, ...props }, ref) => ( +
+)) +Card.displayName = "Card" + +const CardHeader = React.forwardRef(({ className, ...props }, ref) => ( +
+)) +CardHeader.displayName = "CardHeader" + +const CardTitle = React.forwardRef(({ className, ...props }, ref) => ( +
+)) +CardTitle.displayName = "CardTitle" + +const CardDescription = React.forwardRef(({ className, ...props }, ref) => ( +
+)) +CardDescription.displayName = "CardDescription" + +const CardContent = React.forwardRef(({ className, ...props }, ref) => ( +
+)) +CardContent.displayName = "CardContent" + +const CardFooter = React.forwardRef(({ className, ...props }, ref) => ( +
+)) +CardFooter.displayName = "CardFooter" + +export { Card, CardHeader, CardFooter, CardTitle, CardDescription, CardContent } diff --git a/frontend/src/components/ui/input.jsx b/frontend/src/components/ui/input.jsx new file mode 100644 index 0000000000000000000000000000000000000000..879e612dc6be68af99d5d17c0faf77d4e00918d6 --- /dev/null +++ b/frontend/src/components/ui/input.jsx @@ -0,0 +1,19 @@ +import * as React from "react" + +import { cn } from "@/lib/utils" + +const Input = React.forwardRef(({ className, type, ...props }, ref) => { + return ( + + ); +}) +Input.displayName = "Input" + +export { Input } diff --git a/frontend/src/components/ui/scroll-area.jsx b/frontend/src/components/ui/scroll-area.jsx new file mode 100644 index 0000000000000000000000000000000000000000..dc3d3840ca9a57627ec2edcc938111a877c67761 --- /dev/null +++ b/frontend/src/components/ui/scroll-area.jsx @@ -0,0 +1,38 @@ +import * as React from "react" +import * as ScrollAreaPrimitive from "@radix-ui/react-scroll-area" + +import { cn } from "@/lib/utils" + +const ScrollArea = React.forwardRef(({ className, children, ...props }, ref) => ( + + + {children} + + + + +)) +ScrollArea.displayName = ScrollAreaPrimitive.Root.displayName + +const ScrollBar = React.forwardRef(({ className, orientation = "vertical", ...props }, ref) => ( + + + +)) +ScrollBar.displayName = ScrollAreaPrimitive.ScrollAreaScrollbar.displayName + +export { ScrollArea, ScrollBar } diff --git a/frontend/src/components/ui/separator.jsx b/frontend/src/components/ui/separator.jsx new file mode 100644 index 0000000000000000000000000000000000000000..c40b88839ea28b3755355e6a9841301315abf908 --- /dev/null +++ b/frontend/src/components/ui/separator.jsx @@ -0,0 +1,23 @@ +import * as React from "react" +import * as SeparatorPrimitive from "@radix-ui/react-separator" + +import { cn } from "@/lib/utils" + +const Separator = React.forwardRef(( + { className, orientation = "horizontal", decorative = true, ...props }, + ref +) => ( + +)) +Separator.displayName = SeparatorPrimitive.Root.displayName + +export { Separator } diff --git a/frontend/src/components/ui/switch.jsx b/frontend/src/components/ui/switch.jsx new file mode 100644 index 0000000000000000000000000000000000000000..e0b4e8e65d55179868f9e0265590b27dc680ba79 --- /dev/null +++ b/frontend/src/components/ui/switch.jsx @@ -0,0 +1,24 @@ +"use client" + +import * as React from "react" +import * as SwitchPrimitives from "@radix-ui/react-switch" + +import { cn } from "@/lib/utils" + +const Switch = React.forwardRef(({ className, ...props }, ref) => ( + + + +)) +Switch.displayName = SwitchPrimitives.Root.displayName + +export { Switch } diff --git a/frontend/src/components/ui/table.jsx b/frontend/src/components/ui/table.jsx new file mode 100644 index 0000000000000000000000000000000000000000..31b58edcad1e1684e5feff2089c5ff3ffc6f2f6b --- /dev/null +++ b/frontend/src/components/ui/table.jsx @@ -0,0 +1,83 @@ +import * as React from "react" + +import { cn } from "@/lib/utils" + +const Table = React.forwardRef(({ className, ...props }, ref) => ( +
+ + +)) +Table.displayName = "Table" + +const TableHeader = React.forwardRef(({ className, ...props }, ref) => ( + +)) +TableHeader.displayName = "TableHeader" + +const TableBody = React.forwardRef(({ className, ...props }, ref) => ( + +)) +TableBody.displayName = "TableBody" + +const TableFooter = React.forwardRef(({ className, ...props }, ref) => ( + tr]:last:border-b-0", className)} + {...props} /> +)) +TableFooter.displayName = "TableFooter" + +const TableRow = React.forwardRef(({ className, ...props }, ref) => ( + +)) +TableRow.displayName = "TableRow" + +const TableHead = React.forwardRef(({ className, ...props }, ref) => ( +
+)) +TableHead.displayName = "TableHead" + +const TableCell = React.forwardRef(({ className, ...props }, ref) => ( + +)) +TableCell.displayName = "TableCell" + +const TableCaption = React.forwardRef(({ className, ...props }, ref) => ( +
+)) +TableCaption.displayName = "TableCaption" + +export { + Table, + TableHeader, + TableBody, + TableFooter, + TableHead, + TableRow, + TableCell, + TableCaption, +} diff --git a/frontend/src/components/ui/tabs.jsx b/frontend/src/components/ui/tabs.jsx new file mode 100644 index 0000000000000000000000000000000000000000..baa34224cd6439ab57721f3184a48decd972f1bb --- /dev/null +++ b/frontend/src/components/ui/tabs.jsx @@ -0,0 +1,41 @@ +import * as React from "react" +import * as TabsPrimitive from "@radix-ui/react-tabs" + +import { cn } from "@/lib/utils" + +const Tabs = TabsPrimitive.Root + +const TabsList = React.forwardRef(({ className, ...props }, ref) => ( + +)) +TabsList.displayName = TabsPrimitive.List.displayName + +const TabsTrigger = React.forwardRef(({ className, ...props }, ref) => ( + +)) +TabsTrigger.displayName = TabsPrimitive.Trigger.displayName + +const TabsContent = React.forwardRef(({ className, ...props }, ref) => ( + +)) +TabsContent.displayName = TabsPrimitive.Content.displayName + +export { Tabs, TabsList, TabsTrigger, TabsContent } diff --git a/frontend/src/context/AlertContext.jsx b/frontend/src/context/AlertContext.jsx new file mode 100644 index 0000000000000000000000000000000000000000..4ebda8617c69b40efb1712edf1d2feed7a865fef --- /dev/null +++ b/frontend/src/context/AlertContext.jsx @@ -0,0 +1,36 @@ +import { createContext, useState, useRef } from "react"; + +export const AlertContext = createContext(); + +export function AlertProvider({ children }) { + const [alerts, setAlerts] = useState([]); + const lastAlertTime = useRef({}); // per-class cooldown + + const COOLDOWN = 8000; // 8 seconds per class + + const pushAlert = (msg, type = "info", key = "general") => { + const now = Date.now(); + + if (lastAlertTime.current[key] && now - lastAlertTime.current[key] < COOLDOWN) + return; // skip spam alerts + + lastAlertTime.current[key] = now; + + const id = now; + setAlerts(prev => [...prev, { id, msg, type }]); + + setTimeout(() => { + setAlerts(prev => prev.filter(a => a.id !== id)); + }, 5000); + }; + + const removeAlert = (id) => { + setAlerts(prev => prev.filter(a => a.id !== id)); + }; + + return ( + + {children} + + ); +} diff --git a/frontend/src/context/AuthContext.jsx b/frontend/src/context/AuthContext.jsx new file mode 100644 index 0000000000000000000000000000000000000000..dd1dbd46e6e7d6fdcedd3a0f7d71e5f8b02f4e99 --- /dev/null +++ b/frontend/src/context/AuthContext.jsx @@ -0,0 +1,158 @@ +// src/context/AuthContext.jsx +import React, { createContext, useContext, useEffect, useState } from "react"; +import { auth, db } from "../firebaseConfig"; +import { + createUserWithEmailAndPassword, + signInWithEmailAndPassword, + signOut, + onAuthStateChanged, + sendEmailVerification, + updateProfile, +} from "firebase/auth"; +import { + doc, + getDoc, + setDoc, + onSnapshot, + serverTimestamp, +} from "firebase/firestore"; +import toast from "react-hot-toast"; + +const AuthContext = createContext(); + +export function useAuth() { + return useContext(AuthContext); +} + +const defaultSettings = { + theme: "Cyber Blue", + autoRefresh: true, + soundAlerts: true, + aiSensitivity: 70, + responseMode: "Semi-Auto", + ipConfig: "127.0.0.1", + portConfig: "5000", +}; + +export function AuthProvider({ children }) { + const [user, setUser] = useState(null); + const [authLoading, setAuthLoading] = useState(true); + const [settings, setSettings] = useState(defaultSettings); + const [settingsUnsub, setSettingsUnsub] = useState(null); + + useEffect(() => { + const unsub = onAuthStateChanged(auth, async (u) => { + setAuthLoading(true); + if (u) { + setUser(u); + // load settings from Firestore + const docRef = doc(db, "users", u.uid); + const docSnap = await getDoc(docRef); + if (!docSnap.exists()) { + // create default settings for new user + await setDoc(docRef, { + settings: defaultSettings, + createdAt: serverTimestamp(), + }); + setSettings(defaultSettings); + } else { + const d = docSnap.data(); + setSettings(d.settings || defaultSettings); + } + // real-time listener — updates settings in UI when changed elsewhere + const unsubSettings = onSnapshot(docRef, (snap) => { + if (snap.exists()) { + const data = snap.data(); + if (data.settings) setSettings(data.settings); + } + }); + setSettingsUnsub(() => unsubSettings); + } else { + // user logged out + setUser(null); + setSettings(defaultSettings); + if (settingsUnsub) settingsUnsub(); + } + setAuthLoading(false); + }); + return () => unsub(); + // eslint-disable-next-line + }, []); + + // register + async function register(email, password, displayName) { + const creds = await createUserWithEmailAndPassword(auth, email, password); + // optional: set displayName + if (displayName) { + try { + await updateProfile(creds.user, { displayName }); + } catch (e) { + console.warn("Update profile failed:", e); + } + } + // send verification (optional) + try { + await sendEmailVerification(creds.user); + toast.success("Verification email sent (check inbox)."); + } catch {} + return creds.user; + } + + // login + async function login(email, password) { + const creds = await signInWithEmailAndPassword(auth, email, password); + return creds.user; + } + + // logout + async function logout() { + if (settingsUnsub) settingsUnsub(); + await signOut(auth); + } + + // Save settings to Firestore for current user + async function saveSettings(newSettings) { + if (!user) return; + const docRef = doc(db, "users", user.uid); + await setDoc(docRef, { settings: newSettings }, { merge: true }); + setSettings(newSettings); + toast.success("Settings synced to cloud"); + } + + const value = { + user, + authLoading, + settings, + setSettings, + saveSettings, + register, + login, + logout, + }; + + + // 🟢 Apply theme globally whenever settings.theme changes +useEffect(() => { + if (!settings?.theme) return; + + const body = document.body; + body.classList.remove("theme-cyber", "theme-crimson", "theme-emerald", "theme-default"); + + switch (settings.theme) { + case "Cyber Blue": + body.classList.add("theme-cyber"); + break; + case "Crimson Dark": + body.classList.add("theme-crimson"); + break; + case "Emerald Matrix": + body.classList.add("theme-emerald"); + break; + default: + body.classList.add("theme-default"); + } +}, [settings.theme]); + + + return {children}; +} diff --git a/frontend/src/context/DataContext.jsx b/frontend/src/context/DataContext.jsx new file mode 100644 index 0000000000000000000000000000000000000000..8f2422a9be1e81a54ec17b8d5b8fb27371cf40f6 --- /dev/null +++ b/frontend/src/context/DataContext.jsx @@ -0,0 +1,65 @@ +import React, { createContext, useState, useEffect } from "react"; +import { getRecent, getStats } from "../api"; +import { socket } from "../socket"; + +export const LiveDataContext = createContext(); + +export function LiveDataProvider({ children }) { + const [rows, setRows] = useState([]); + const [stats, setStats] = useState({}); + const [threatCount, setThreatCount] = useState(0); + const [running, setRunning] = useState(false); + + // Load saved data + useEffect(() => { + const r = JSON.parse(localStorage.getItem("live_rows") || "[]"); + const s = JSON.parse(localStorage.getItem("live_stats") || "{}"); + if (r.length) setRows(r); + if (Object.keys(s).length) setStats(s); + }, []); + + // Fetch from backend on mount + useEffect(() => { + (async () => { + const [r1, r2] = await Promise.all([getRecent(), getStats()]); + setRows(r1?.events || []); + setStats(r2 || {}); + })(); + }, []); + + // Socket listener + useEffect(() => { + socket.on("new_event", (evt) => { + setRows((prev) => { + const updated = [...prev, evt]; + if (updated.length > 300) updated.shift(); + localStorage.setItem("live_rows", JSON.stringify(updated)); + return updated; + }); + setStats((prev) => { + const updated = { ...prev }; + const label = (evt.prediction || "UNKNOWN").toUpperCase(); + if (label !== "UNKNOWN") updated[label] = (updated[label] || 0) + 1; + localStorage.setItem("live_stats", JSON.stringify(updated)); + return updated; + }); + if (["TOR", "I2P", "ZERONET"].includes(evt.prediction?.toUpperCase())) + setThreatCount((c) => c + 1); + }); + return () => socket.off("new_event"); + }, []); + + return ( + + {children} + + ); +} diff --git a/frontend/src/data/attackSamples.js b/frontend/src/data/attackSamples.js new file mode 100644 index 0000000000000000000000000000000000000000..b39abdbfebaf376ac1530920a19c39fe88859dc3 --- /dev/null +++ b/frontend/src/data/attackSamples.js @@ -0,0 +1,56 @@ +// src/data/attackSamples.js +// Option A: arrays-only. Feature order MUST match CICIDS_FEATURES below. + +export const CICIDS_SAMPLES = { + "BENIGN": [ + 6, 80, 200000, 10, 8, 640, 512, 64, 64, 50000, 0, 0, 50000 + ], + "DoS attacks-Hulk": [ + 6, 80, 20000, 1500, 50, 900000, 2400, 600, 48, 100, 500, 0, 100 + ], + "DoS attacks-SlowHTTPTest": [ + 6, 80, 10000000, 5, 5, 200, 200, 40, 40, 1000000, 0, 0, 1000000 + ], + "DoS attacks-Slowloris": [ + 6, 80, 12000000, 3, 3, 150, 150, 50, 50, 2000000, 0, 0, 2000000 + ], + "DoS attacks-GoldenEye": [ + 6, 80, 500000, 400, 40, 24000, 3200, 60, 80, 10000, 100, 0, 10000 + ], + "Bot": [ + 6, 8080, 5000000, 20, 20, 1024, 1024, 51, 51, 200000, 1, 0, 200000 + ], + "Infilteration": [ + 6, 445, 20000000, 3, 1, 120, 40, 40, 40, 3000000, 0, 0, 3000000 + ], + "FTP-BruteForce": [ + 6, 21, 1000000, 6, 6, 300, 300, 50, 50, 100000, 0, 0, 100000 + ], + "SSH-Bruteforce": [ + 6, 22, 2000000, 8, 8, 560, 560, 70, 70, 300000, 0, 0, 300000 + ], + "DDOS attack-HOIC": [ + 6, 80, 20000, 500, 10, 300000, 600, 600, 60, 200, 200, 0, 200 + ], + "DDOS attack-LOIC-UDP": [ + 17, 80, 10000, 200, 0, 14000, 0, 70, 0, 500, 0, 0, 500 + ], + "Brute Force -Web": [ + 6, 80, 8000000, 2, 2, 150, 150, 75, 75, 1000000, 0, 0, 1000000 + ], + "Brute Force -XSS": [ + 6, 80, 5000000, 2, 2, 120, 120, 60, 60, 2000000, 0, 0, 2000000 + ], + "SQL Injection": [ + 6, 80, 15000000, 3, 3, 200, 200, 66, 66, 3000000, 0, 0, 3000000 + ] +}; + +export const BCC_SAMPLES = { + "BENIGN": [6, 52344, 443, 0.18, 8, 6, 24, 1200, 60, 1200/0.18, 0.02, 0, 1, 0, 1], + "VPN": [17, 52100, 1194, 30, 400, 380, 0, 1400, 28, (1400*400)/30, 0.075, 0, 0, 0, 0], + "TOR": [6, 52321, 9001, 12, 200, 195, 24, 512, 40, (512*200)/12, 0.06, 0, 1, 0, 0], + "I2P": [6, 40000, 7656, 40, 500, 500, 24, 256, 40, (256*500)/40, 0.04, 0, 1, 0, 0], + "FREENET": [6, 52000, 8888, 8, 60, 58, 24, 400, 40, (400*60)/8, 0.12, 0, 1, 0, 1], + "ZERONET": [6, 51000, 8888, 3, 10, 9, 24, 300, 40, (300*10)/3, 0.3, 0, 1, 0, 1] +}; \ No newline at end of file diff --git a/frontend/src/data/featureInfo.js b/frontend/src/data/featureInfo.js new file mode 100644 index 0000000000000000000000000000000000000000..79a95785e936399306324c7524d37c8c12735db0 --- /dev/null +++ b/frontend/src/data/featureInfo.js @@ -0,0 +1,35 @@ +// src/data/featureInfo.js + +export const FEATURE_INFO = { + // CICIDS features + "Protocol": "Transport layer protocol. 6 = TCP, 17 = UDP.", + "Dst Port": "Destination port number of the network flow.", + "Flow Duration": "Total duration of the flow in seconds.", + "Tot Fwd Pkts": "Total number of packets sent forward.", + "Tot Bwd Pkts": "Total number of packets sent backward.", + "TotLen Fwd Pkts": "Total bytes sent forward.", + "TotLen Bwd Pkts": "Total bytes sent backward.", + "Fwd Pkt Len Mean": "Mean length of forward packets.", + "Bwd Pkt Len Mean": "Mean length of backward packets.", + "Flow IAT Mean": "Mean inter-arrival time between packets.", + "Fwd PSH Flags": "Number of TCP PUSH flags (PSH).", + "Fwd URG Flags": "Number of TCP URG flags (urgent).", + "Fwd IAT Mean": "Mean inter-arrival time of forward packets.", + + // BCC features + "proto": "Network protocol. 6 = TCP, 17 = UDP.", + "src_port": "Source port of the traffic flow.", + "dst_port": "Destination port of the traffic flow.", + "flow_duration": "Duration of the observed flow.", + "total_fwd_pkts": "Packets flowed forward.", + "total_bwd_pkts": "Packets flowed backward.", + "flags_numeric": "TCP flag combination encoded numerically.", + "payload_len": "Payload bytes in flow.", + "header_len": "Header bytes in flow.", + "rate": "Data throughput rate (bytes/sec).", + "iat": "Inter-arrival time between packets.", + "syn": "Count of SYN flags.", + "ack": "Count of ACK flags.", + "rst": "Count of RST flags.", + "fin": "Count of FIN flags." +}; diff --git a/frontend/src/firebaseConfig.js b/frontend/src/firebaseConfig.js new file mode 100644 index 0000000000000000000000000000000000000000..7d16f5da51816a8cb14a5f0588416a66248322e7 --- /dev/null +++ b/frontend/src/firebaseConfig.js @@ -0,0 +1,34 @@ +// ✅ src/firebaseConfig.js + +// Import the Firebase SDK modules you need +import { initializeApp } from "firebase/app"; +import { + getAuth, + setPersistence, + browserLocalPersistence +} from "firebase/auth"; +import { getFirestore } from "firebase/firestore"; + +// ✅ Replace these values with your Firebase project config +const firebaseConfig = { + apiKey: "AIzaSyB5koj8scMrv1v_hyl3wPLKouuZI3dWA6g", + authDomain: "network-intrusion-detect-ce914.firebaseapp.com", + projectId: "network-intrusion-detect-ce914", + storageBucket: "network-intrusion-detect-ce914.firebasestorage.app", + messagingSenderId: "1078562682443", + appId: "1:1078562682443:web:80869f04cbdce16616bfce", +}; + +// ✅ Initialize the Firebase app +const app = initializeApp(firebaseConfig); + +// ✅ Initialize Auth & Firestore +const auth = getAuth(app); +setPersistence(auth, browserLocalPersistence); // keeps login after refresh + +const db = getFirestore(app); + +// ✅ Export them +export { app, auth, db }; + + diff --git a/frontend/src/index.css b/frontend/src/index.css new file mode 100644 index 0000000000000000000000000000000000000000..e41ac38ca22ab0e8c75686d4ba5f3422eff726cc --- /dev/null +++ b/frontend/src/index.css @@ -0,0 +1,499 @@ +/* ------------------------- + LIGHT THEME VARIABLES +------------------------- */ +:root { + --bg: #f8fafc; + --text: #0f172a; + --card: #ffffff; + --accent: #2563eb; +} + +/* ------------------------- + DARK THEME VARIABLES +------------------------- */ +.dark { + --bg: #0a0f1c; + --text: #e2e8f0; + --card: #0f172a; + --accent: #00e5ff; +} + +/* Smooth transitions */ +body { + transition: background-color 0.3s ease, color 0.3s ease; +} + +@tailwind base; +@tailwind components; +@tailwind utilities; + + +:root { + color-scheme: light; +} +.dark { + color-scheme: dark; +} + + + +body { + background-color: var(--bg); + color: var(--text); + min-height: 100vh; + background-image: radial-gradient(ellipse at top left, rgba(0,229,255,.05), transparent 40%), + radial-gradient(ellipse at bottom right, rgba(167,139,250,.05), transparent 40%); + transition: background-color 0.3s ease, color 0.3s ease; +} + + + +.cyber-card { + background-color: var(--card); + @apply backdrop-blur rounded-2xl p-4 shadow-neon border border-cyan-400/10; +} + + +.neon-text { text-shadow: 0 0 8px rgba(0,229,255,.55); } + +.tooltip { + backdrop-filter: blur(6px); + background: rgba(15, 23, 42, 0.6); + color: white; + box-shadow: 0 0 10px rgba(0, 229, 255, 0.3); + border: 1px solid rgba(0, 229, 255, 0.3); + border-radius: 10px; + padding: 10px 14px; + font-size: 0.9rem; + z-index: 50; +} + + +@keyframes glowPulse { + 0%, 100% { + box-shadow: 0 0 8px rgba(0, 229, 255, 0.3); + } + 50% { + box-shadow: 0 0 16px rgba(0, 229, 255, 0.6); + } +} + +.animate-glowPulse { + animation: glowPulse 3s infinite ease-in-out; +} + +.flow-wrap svg { background: linear-gradient(180deg, rgba(2,6,23,0.3), rgba(2,6,23,0.15)); border-radius: 6px; } +.flow-wrap rect { filter: drop-shadow(0 3px 12px rgba(0,0,0,0.6)); } + + +@keyframes gridMove { + 0% { background-position: 0 0, 0 0; } + 100% { background-position: 22px 22px, 22px 22px; } +} +.animate-grid { + animation: gridMove 8s linear infinite; +} +@keyframes fadeIn { + from { opacity: 0; transform: translateY(10px); } + to { opacity: 1; transform: translateY(0); } +} +.animate-fadeIn { + animation: fadeIn 0.3s ease-out; +} + +@keyframes pulse-slow { + 0%, 100% { opacity: 0.6; } + 50% { opacity: 1; } +} +.animate-pulse-slow { + animation: pulse-slow 8s ease-in-out infinite; +} + +/* In index.css */ +canvas { + filter: drop-shadow(0 0 25px rgba(0, 229, 255, 0.3)); + position: fixed; /* Do NOT override Tailwind */ + inset: 0; + z-index: -1; + pointer-events: none; +} + + +/* 🌈 THEME VARIANTS */ +.theme-cyber { + --bg: #010b14; + --text: #aeeaff; + --card: rgba(0, 15, 30, 0.7); + --accent: #00e5ff; +} + +.theme-crimson { + --bg: #100006; + --text: #ffccd5; + --card: rgba(20, 0, 10, 0.7); + --accent: #ff0044; +} + +.theme-emerald { + --bg: #00150c; + --text: #b0ffd6; + --card: rgba(0, 30, 15, 0.7); + --accent: #00ff99; +} + +.theme-default { + --bg: #0a0a0a; + --text: #dddddd; + --card: rgba(25, 25, 25, 0.6); + --accent: #8888ff; +} + +/* 🎨 Base Styles Using Variables */ +body { + background-color: var(--bg); + color: var(--text); + transition: background-color 0.4s ease, color 0.4s ease; +} + +.card-glow { + background-color: var(--card); + border: 1px solid var(--accent); + box-shadow: 0 0 20px color-mix(in srgb, var(--accent) 25%, transparent); + transition: all 0.3s ease; +} + +.text-accent { + color: var(--accent); +} + +.border-accent { + border-color: var(--accent); +} + +.bg-accent { + background-color: color-mix(in srgb, var(--accent) 15%, transparent); +} + +/* ---------- HERO + GLASS LAYER STYLES ---------- */ +.hero-wrapper { + position: relative; + overflow: visible; +} + +.hero-bg-layer { + position: absolute; + inset: 0; + height: 360px; + pointer-events: none; + z-index: 0; + background: radial-gradient(800px 200px at 10% 10%, rgba(0,229,255,0.06), transparent), + radial-gradient(600px 200px at 90% 30%, rgba(167,139,250,0.05), transparent), + linear-gradient(180deg, rgba(2,6,23,0.8), transparent 60%); + filter: blur(40px); +} + +/* hero inner panel / right artwork */ +.hero-panel { + background: linear-gradient(180deg, rgba(5,10,18,0.65), rgba(6,8,20,0.35)); + border-radius: 1rem; + box-shadow: 0 10px 40px rgba(2,6,23,0.6), inset 0 1px 0 rgba(255,255,255,0.02); + border: 1px solid rgba(0,229,255,0.12); +} + +/* 3D floating cubes */ +.cube-1, .cube-2, .cube-3 { + border-radius: 10%; + background: linear-gradient(135deg, rgba(139,92,246,0.25), rgba(0,229,255,0.12)); + box-shadow: 0 16px 48px rgba(2,6,23,0.8), 0 2px 8px rgba(0,229,255,0.06); + transform: rotateY(12deg) rotateX(8deg); +} + +/* simple block 3d elements used inside hero-panel */ +.block-3d { + background: linear-gradient(180deg, rgba(255,255,255,0.03), rgba(255,255,255,0.01)); + border-radius: 0.6rem; + border: 1px solid rgba(0,229,255,0.12); + box-shadow: 0 10px 30px rgba(2,6,23,0.65), 0 0 30px rgba(0,229,255,0.06); + backdrop-filter: blur(6px); +} + +/* glass shell that holds the dashboard section under the hero */ +.glass-shell { + background: linear-gradient(180deg, rgba(2,6,23,0.6), rgba(2,6,23,0.35)); + border-radius: 1.2rem; + box-shadow: 0 30px 60px rgba(2,6,23,0.6), 0 4px 18px rgba(0,229,255,0.03); + border: 1px solid rgba(0,229,255,0.06); + backdrop-filter: blur(8px); +} + +/* neon text and small tweaks */ +.hero-text .neon-text { + text-shadow: 0 0 12px rgba(0,229,255,0.6); +} + +.hero-inner .hero-art { min-height: 320px; } + +/* responsive spacing */ +@media (max-width: 768px) { + .hero-inner { padding-top: 6rem; padding-bottom: 3rem; } + .hero-panel { height: 240px; } +} + +/* a little floating animation for cubes */ +.cube-1 { animation: floaty 7s ease-in-out infinite; opacity: 0.9; } +.cube-2 { animation: floaty 8s ease-in-out infinite; opacity: 0.85; } +.cube-3 { animation: floaty 6s ease-in-out infinite; opacity: 0.8; } + +@keyframes floaty { + 0% { transform: translateY(0) rotate(6deg); } + 50% { transform: translateY(-8px) rotate(4deg); } + 100% { transform: translateY(0) rotate(6deg); } +} + +/* ensure hero sits above background glow */ +.hero-wrapper { z-index: 10; } + + +/* Hologram floating blocks */ +.block-holo { + background: linear-gradient( + 135deg, + rgba(139, 92, 246, 0.28), + rgba(0, 229, 255, 0.22) + ); + box-shadow: + 0 18px 50px rgba(2, 6, 23, 0.8), + 0 0 40px rgba(0, 229, 255, 0.18), + inset 0 0 20px rgba(255, 255, 255, 0.08); + animation: holoFloat 6s ease-in-out infinite; + filter: saturate(140%); +} + +@keyframes holoFloat { + 0% { transform: translateY(0px) rotate(6deg); } + 50% { transform: translateY(-10px) rotate(3deg); } + 100% { transform: translateY(0px) rotate(6deg); } +} + +/* Stacked 3D tiles */ +.tile-3d { + background: linear-gradient( + 160deg, + rgba(255, 255, 255, 0.04), + rgba(0, 229, 255, 0.05) + ); + border: 1px solid rgba(0, 229, 255, 0.2); + backdrop-filter: blur(8px); + border-radius: 0.85rem; + box-shadow: + 0 14px 35px rgba(2, 6, 23, 0.5), + 0 0 25px rgba(0,229,255,0.09); + transform: perspective(800px) rotateX(25deg); +} + + +.hero-panel { + backdrop-filter: blur(12px); + border-radius: 24px; +} + + +.topbar-button { + backdrop-filter: blur(6px); + box-shadow: 0 0 20px rgba(0,229,255,0.15); + transition: all 0.2s ease; +} + +.topbar-button:hover { + box-shadow: 0 0 25px rgba(0,229,255,0.3); + transform: translateY(-2px); +} + + +@keyframes waveMove { + 0% { transform: translateX(0); } + 100% { transform: translateX(-50%); } +} + +.neon-wave { + width: 200%; + height: 60px; + background: repeating-linear-gradient( + 90deg, + rgba(0,229,255,0.8) 0px, + rgba(0,229,255,0.8) 2px, + transparent 2px, + transparent 6px + ); + mask-image: url("data:image/svg+xml;utf8,"); + mask-size: 200% 100%; + mask-repeat: repeat-x; + animation: waveMove 4s linear infinite; + opacity: 0.7; +} + + +@keyframes spin-slow { + 0% { transform: rotate(0deg); } + 100% { transform: rotate(360deg); } +} + +.animate-spin-slow { + animation: spin-slow 18s linear infinite; +} + +@keyframes float-slow { + 0% { transform: translateY(0px) rotate(0deg); } + 50% { transform: translateY(-15px) rotate(3deg); } + 100% { transform: translateY(0px) rotate(0deg); } +} + +.animate-float-slow { + animation: float-slow 6s ease-in-out infinite; +} + + +@keyframes drift { + 0% { + transform: translate(0px, 0px); + } + 50% { + transform: translate(15px, -10px); + } + 100% { + transform: translate(0px, 0px); + } +} + +.drift-slow { + animation: drift 10s ease-in-out infinite; +} + + +/* Neon Scrollbar */ +.custom-scroll::-webkit-scrollbar { + width: 6px; +} + +.custom-scroll::-webkit-scrollbar-track { + background: rgba(0, 229, 255, 0.05); + border-radius: 10px; +} + +.custom-scroll::-webkit-scrollbar-thumb { + background: rgba(0, 229, 255, 0.4); + border-radius: 10px; + box-shadow: 0 0 10px rgba(0, 229, 255, 0.6); +} + +.custom-scroll::-webkit-scrollbar-thumb:hover { + background: rgba(0, 229, 255, 0.7); +} + +/* Show thinner scrollbar in Firefox and set thumb color */ +.custom-scroll { + scrollbar-width: thin; + scrollbar-color: rgba(0,229,255,0.4) rgba(0,229,255,0.05); + -ms-overflow-style: auto; +} + +/* Scroll fade overlays to hint at scrollable content */ +.scroll-fade-top { + background: linear-gradient(180deg, rgba(2,6,23,0.85), transparent 60%); +} +.scroll-fade-bottom { + background: linear-gradient(0deg, rgba(2,6,23,0.85), transparent 60%); +} + +/* Floating buttons styling */ +.floating-controls { + box-shadow: 0 10px 30px rgba(0, 229, 255, 0.08), 0 6px 12px rgba(2,6,23,0.6); + border: 1px solid rgba(0,229,255,0.08); +} + +/* Larger neon action buttons */ +.neon-btn { + background: linear-gradient(90deg, rgba(0,229,255,0.06), rgba(139,92,246,0.04)); + border: 1px solid rgba(0,229,255,0.18); + box-shadow: 0 8px 30px rgba(0,229,255,0.08), inset 0 1px 0 rgba(255,255,255,0.02); +} +.neon-btn:hover { + box-shadow: 0 12px 40px rgba(0,229,255,0.14), inset 0 1px 0 rgba(255,255,255,0.02); +} + +.danger-btn { + background: linear-gradient(90deg, rgba(255,80,80,0.12), rgba(255,80,80,0.06)); + border: 1px solid rgba(255,80,80,0.18); + box-shadow: 0 8px 30px rgba(255,80,80,0.06); +} +.danger-btn:hover { box-shadow: 0 12px 40px rgba(255,80,80,0.12); } + +/* Tab accent glow for active state */ +.tab-active-glow { + box-shadow: 0 6px 22px rgba(0,229,255,0.12); +} + +/* Slightly glow scrollbar thumb for non-webkit fallbacks using scrollbar-color */ +html { scrollbar-color: rgba(0,229,255,0.45) rgba(0,0,0,0.15); } + + + +@layer base { + .shadcn{ + + --background: 0 0% 100%; + --foreground: 222.2 84% 4.9%; + --card: 0 0% 100%; + --card-foreground: 222.2 84% 4.9%; + --popover: 0 0% 100%; + --popover-foreground: 222.2 84% 4.9%; + --primary: 222.2 47.4% 11.2%; + --primary-foreground: 210 40% 98%; + --secondary: 210 40% 96.1%; + --secondary-foreground: 222.2 47.4% 11.2%; + --muted: 210 40% 96.1%; + --muted-foreground: 215.4 16.3% 46.9%; + --accent: 210 40% 96.1%; + --accent-foreground: 222.2 47.4% 11.2%; + --destructive: 0 84.2% 60.2%; + --destructive-foreground: 210 40% 98%; + --border: 214.3 31.8% 91.4%; + --input: 214.3 31.8% 91.4%; + --ring: 222.2 84% 4.9%; + --chart-1: 12 76% 61%; + --chart-2: 173 58% 39%; + --chart-3: 197 37% 24%; + --chart-4: 43 74% 66%; + --chart-5: 27 87% 67%; + --radius: 0.5rem; + } + shadcn.dark { + --background: 222.2 84% 4.9%; + --foreground: 210 40% 98%; + --card: 222.2 84% 4.9%; + --card-foreground: 210 40% 98%; + --popover: 222.2 84% 4.9%; + --popover-foreground: 210 40% 98%; + --primary: 210 40% 98%; + --primary-foreground: 222.2 47.4% 11.2%; + --secondary: 217.2 32.6% 17.5%; + --secondary-foreground: 210 40% 98%; + --muted: 217.2 32.6% 17.5%; + --muted-foreground: 215 20.2% 65.1%; + --accent: 217.2 32.6% 17.5%; + --accent-foreground: 210 40% 98%; + --destructive: 0 62.8% 30.6%; + --destructive-foreground: 210 40% 98%; + --border: 217.2 32.6% 17.5%; + --input: 217.2 32.6% 17.5%; + --ring: 212.7 26.8% 83.9%; + --chart-1: 220 70% 50%; + --chart-2: 160 60% 45%; + --chart-3: 30 80% 55%; + --chart-4: 280 65% 60%; + --chart-5: 340 75% 55%; + } +} + + + diff --git a/frontend/src/layouts/MainLayout.jsx b/frontend/src/layouts/MainLayout.jsx new file mode 100644 index 0000000000000000000000000000000000000000..b2b6e7fe65b7042511f9e5151da58f52f61d45bf --- /dev/null +++ b/frontend/src/layouts/MainLayout.jsx @@ -0,0 +1,23 @@ +// src/layouts/MainLayout.jsx +import Sidebar from "../components/Sidebar"; + +export default function MainLayout({ children }) { + return ( +
+ + {/* Sidebar (glass) */} + + + {/* Main content should NOT override background */} +
+ {children} +
+ + {/* Transparent background carrier */} +
+
+ ); +} diff --git a/frontend/src/lib/utils.js b/frontend/src/lib/utils.js new file mode 100644 index 0000000000000000000000000000000000000000..b20bf017182a344cc7cd55ce40eb2e19752a42df --- /dev/null +++ b/frontend/src/lib/utils.js @@ -0,0 +1,6 @@ +import { clsx } from "clsx"; +import { twMerge } from "tailwind-merge" + +export function cn(...inputs) { + return twMerge(clsx(inputs)); +} diff --git a/frontend/src/main.jsx b/frontend/src/main.jsx new file mode 100644 index 0000000000000000000000000000000000000000..9c47f2d99065bf66eb96428f3bbdd181a97fa20d --- /dev/null +++ b/frontend/src/main.jsx @@ -0,0 +1,20 @@ +import React from "react"; +import ReactDOM from "react-dom/client"; +import App from "./App.jsx"; +import "./index.css"; +import { ThemeProvider } from "./theme.jsx"; // ✅ ADD THIS +import { AlertProvider } from "./context/AlertContext.jsx"; +import { applySavedTheme } from "./themeManager"; + +applySavedTheme(); + + +ReactDOM.createRoot(document.getElementById("root")).render( + + {/* ✅ WRAP APP */} + {/* ✅ WRAP APP */} + + + + +); diff --git a/frontend/src/pages/ChatAssistant.jsx b/frontend/src/pages/ChatAssistant.jsx new file mode 100644 index 0000000000000000000000000000000000000000..3883808b8bcf363f60a59abab6d4c0d3a7ac80dd --- /dev/null +++ b/frontend/src/pages/ChatAssistant.jsx @@ -0,0 +1,265 @@ +import React, { useState, useEffect, useRef } from "react"; +import { MessageCircle, X, Sparkles } from "lucide-react"; +import { BASE_URL } from "../api.js"; + +// Display name for the model (shown in header) +const CHAT_MODEL_LABEL = "Llama-3.1-8B-Instant (Groq)"; + +export default function ChatAssistant() { + const [isOpen, setIsOpen] = useState(false); + const [messages, setMessages] = useState([ + { + role: "assistant", + content: + "Hello, I’m your NIDS assistant. Ask me about threats, logs, models, or anything security-related! ⚡", + }, + ]); + const [input, setInput] = useState(""); + const [loading, setLoading] = useState(false); + const [errorText, setErrorText] = useState(""); + + const messagesEndRef = useRef(null); + const bootAudioRef = useRef(null); + + // -------- Boot sound when opening ---------- + useEffect(() => { + if (isOpen) { + // lazy init audio + if (!bootAudioRef.current) { + bootAudioRef.current = new Audio("../../ai_bootup.mp3"); + } + try { + bootAudioRef.current.currentTime = 0; + bootAudioRef.current.play(); + } catch (e) { + // ignore autoplay errors + console.warn("Boot sound blocked by browser:", e); + } + } + }, [isOpen]); + + // -------- Auto-scroll to latest message ---------- + useEffect(() => { + if (messagesEndRef.current) { + messagesEndRef.current.scrollIntoView({ behavior: "smooth" }); + } + }, [messages, loading]); + + // -------- Send message to backend ---------- + const handleSend = async () => { + const trimmed = input.trim(); + if (!trimmed || loading) return; + + setErrorText(""); + setLoading(true); + + const userMessage = { role: "user", content: trimmed }; + setMessages((prev) => [...prev, userMessage]); + setInput(""); + + try { + const res = await fetch(`${BASE_URL}/api/chat`, { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify({ message: trimmed }), + }); + + if (!res.ok) { + const txt = await res.text(); + throw new Error(`HTTP ${res.status}: ${txt}`); + } + + const data = await res.json(); + const replyText = + typeof data.reply === "string" + ? data.reply + : "I received your message, but the reply format was unexpected."; + + setMessages((prev) => [ + ...prev, + { role: "assistant", content: replyText }, + ]); + } catch (err) { + console.error("Chat error:", err); + setErrorText("AI is unreachable right now. Please try again in a bit."); + setMessages((prev) => [ + ...prev, + { + role: "assistant", + content: + "Sorry, I couldn't reach the AI backend. Check your internet / API key and try again.", + }, + ]); + } finally { + setLoading(false); + } + }; + + const handleKeyDown = (e) => { + if (e.key === "Enter" && !e.shiftKey) { + e.preventDefault(); + handleSend(); + } + }; + + // -------- Small helper for message bubble style ---------- + const bubbleClass = (role) => + role === "user" + ? "bg-cyan-500/20 border border-cyan-400/40 self-end" + : "bg-slate-800/80 border border-slate-600/60 self-start"; + + const textClass = (role) => + role === "user" ? "text-cyan-100" : "text-slate-100"; + + return ( + <> + {/* Floating Jarvis orb button */} + + + {/* Overlay */} + {isOpen && ( +
+ {/* Chat panel */} +
+ {/* Header */} +
+
+ {/* Jarvis orb avatar */} +
+
+ +
+
+
+
+ + NIDS AI Assistant + + + Model:{" "} + {CHAT_MODEL_LABEL} + +
+
+ + +
+ + {/* Messages area */} +
+ {/* Bootup caption */} +

+ System online · You can ask things like{" "} + + “Explain this alert” or “Summarize current threats” + +

+ + {messages.map((m, idx) => ( +
+
+

{m.content}

+
+
+ ))} + + {/* Typing indicator */} + {loading && ( +
+
+ + + +
+
+ )} + +
+
+ + {/* Error text */} + {errorText && ( +
+ {errorText} +
+ )} + + {/* Input area */} +
+
{ + e.preventDefault(); + handleSend(); + }} + > +