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README.md

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----
-title: UEBA Risk Score
-emoji: 👀
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 6.0.2
-app_file: app.py
-pinned: false
-license: ecl-2.0
-short_description: UEBA_Risk_Score
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# UEBA Risk Scoring Demo (Gradio + Hugging Face Spaces)
+
+This repository contains a simple **User & Entity Behavior Analytics (UEBA)** anomaly detection demo. It trains an **IsolationForest** on historical logs, builds per-user baselines, and produces a **blended risk score** for new events (60% model, 40% rules).
+
+> ⚠️ Disclaimer: This is an educational demo. For production use, harden the data pipeline, feature engineering, explainability, and security (PII handling, access controls, audit logging, etc.).
+
+## Quick Start (Local)
+
+1. Create a Python environment and install dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+2. Run the app:
+
+```bash
+python app.py
+```
+
+3. In the UI:
+   - **Train Baseline**: Upload a CSV with columns
+     `user,timestamp,action,success,country,device,ip`
+   - **Score Events**: Upload a CSV with the same columns; get risk scores.
+
+## Deploy on Hugging Face Spaces
+
+1. Go to **Hugging Face → Spaces → New Space**.
+2. Choose **SDK: Gradio**, **Space Type: Public or Private**.
+3. Upload these files:
+   - `app.py` (entry point)
+   - `requirements.txt`
+   - optionally `sample_training.csv` and `sample_events.csv`
+4. The Space will build automatically. Click **App** to use.
+
+> Spaces use `app.py` as the default entrypoint for Gradio apps.
+
+## Data Schema
+
+CSV columns expected (order doesn't matter):
+
+- `user`: string user identifier
+- `timestamp`: ISO8601 or any pandas-parsable datetime
+- `action`: string (e.g., `login`, `file_download`, `admin_change`)
+- `success`: 1 or 0 (use 1 for non-login actions)
+- `country`: country code or name
+- `device`: string device identifier
+- `ip`: string IP identifier
+
+## Features & Signals
+
+**Model Features** (used by IsolationForest):
+- `hour` (0–23)
+- `time_since_last_minutes` (clipped 0–1440)
+- binary flags: `failed_login`, `is_night`, `location_change`, `new_device`, `new_ip`, `rare_action`, `impossible_travel`
+
+**Rule Weights** (blended into risk score):
+- failed_login: +25
+- is_night: +10
+- location_change: +20
+- new_device: +15
+- new_ip: +10
+- rare_action: +10
+- impossible_travel: +25 (location change within <120 minutes since prior event)
+
+Final risk = 0.6 * normalized model anomaly + 0.4 * rules (0–100).
+
+## Sample Data
+
+Two synthetic CSVs are included:
+- `sample_training.csv`: baseline historical events
+- `sample_events.csv`: new events to score
+
+You can upload them directly in the app to test end-to-end.
+
+## Notes & Extensions
+
+- Replace simplified `impossible_travel` with geodistance based on IP geolocation (ensure privacy, compliance).
+- Add domain-specific actions and weights; calibrate with red-team exercises and historical incidents.
+- Use SHAP or feature importance for richer explainability.
+- Consider per-entity models (service accounts, devices) and role-based profiles.
+- Add persistence/versioning of models and baselines; auto-retraining schedules.
+- Integrate alerting (webhooks) and dashboards.
+
+## License
+
+MIT

app.py

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+# app.py
+"""
+UEBA Risk Scoring demo (Gradio + Hugging Face Spaces)
+
+- Train an unsupervised anomaly detector (IsolationForest) on historical logs
+- Build user baseline profiles (devices, IPs, common country, frequent actions)
+- Score new events with a blended risk score (model anomaly + rule signals)
+
+Expected CSV schema for both training and scoring:
+  user,timestamp,action,success,country,device,ip
+Where:
+  - user: string identifier
+  - timestamp: ISO8601 or any pandas-parsable datetime
+  - action: free-form string (e.g., 'login', 'file_download', 'admin_change')
+  - success: 1 or 0 (e.g., login success flag; use 1 for non-login actions)
+  - country: two-letter or name, free-form string
+  - device: string identifier
+  - ip: string identifier
+
+This is a simplified educational demo -- not production security tooling.
+"""
+
+import os
+import pickle
+import json
+from datetime import datetime
+
+import numpy as np
+import pandas as pd
+from sklearn.ensemble import IsolationForest
+from sklearn.preprocessing import MinMaxScaler
+
+import gradio as gr
+
+ARTIFACT_DIR = "artifacts"
+MODEL_PATH = os.path.join(ARTIFACT_DIR, "isolation_forest.pkl")
+PROFILE_PATH = os.path.join(ARTIFACT_DIR, "baseline_profiles.json")
+SCALER_PATH = os.path.join(ARTIFACT_DIR, "feature_scaler.pkl")
+ANOMALY_RANGE_PATH = os.path.join(ARTIFACT_DIR, "anomaly_range.json")
+FEATURES_JSON = os.path.join(ARTIFACT_DIR, "features.json")
+
+os.makedirs(ARTIFACT_DIR, exist_ok=True)
+
+FEATURE_COLUMNS = [
+    "hour",
+    "time_since_last_minutes",
+    "failed_login",
+    "is_night",
+    "location_change",
+    "new_device",
+    "new_ip",
+    "rare_action",
+    "impossible_travel"
+]
+
+RULE_WEIGHTS = {
+    "failed_login": 25,
+    "is_night": 10,
+    "location_change": 20,
+    "new_device": 15,
+    "new_ip": 10,
+    "rare_action": 10,
+    "impossible_travel": 25,
+}
+
+# -----------------
+# Feature Engineering
+# -----------------
+
+def _parse_time(ts):
+    try:
+        return pd.to_datetime(ts, errors='coerce')
+    except Exception:
+        return pd.NaT
+
+
+def build_baseline_profiles(df: pd.DataFrame):
+    """Create per-user baseline: common_country, known_devices, known_ips, action_counts."""
+    profiles = {}
+    for user, g in df.groupby("user"):
+        # common country = mode
+        common_country = g["country"].mode().iloc[0] if not g["country"].mode().empty else None
+        devices = sorted(list(set(g["device"].dropna().astype(str))))
+        ips = sorted(list(set(g["ip"].dropna().astype(str))))
+        action_counts = g["action"].value_counts().to_dict()
+        profiles[user] = {
+            "common_country": common_country,
+            "devices": devices,
+            "ips": ips,
+            "action_counts": action_counts,
+            "total_actions": int(g.shape[0])
+        }
+    return profiles
+
+
+def extract_features(df: pd.DataFrame, profiles: dict):
+    df = df.copy()
+    df["timestamp"] = df["timestamp"].apply(_parse_time)
+    df.sort_values(["user", "timestamp"], inplace=True)
+
+    # Basic fields
+    df["hour"] = df["timestamp"].dt.hour.fillna(0)
+    df["is_night"] = df["hour"].apply(lambda h: 1 if (h <= 5 or h >= 22) else 0)
+    df["failed_login"] = df["success"].apply(lambda x: 1 if str(x) in ["0", 0, False, "False"] else 0)
+
+    # Time since last per user
+    df["time_since_last_minutes"] = 0.0
+    last_time = {}
+    for idx, row in df.iterrows():
+        u = row["user"]
+        t = row["timestamp"]
+        if pd.isna(t):
+            df.at[idx, "time_since_last_minutes"] = 0.0
+        else:
+            if u in last_time and not pd.isna(last_time[u]):
+                delta = (t - last_time[u]).total_seconds() / 60.0
+                df.at[idx, "time_since_last_minutes"] = max(0.0, min(delta, 1440.0))  # clip 0..1 day
+            else:
+                df.at[idx, "time_since_last_minutes"] = 1440.0
+        last_time[u] = t
+
+    # Profile-derived flags
+    df["location_change"] = 0
+    df["new_device"] = 0
+    df["new_ip"] = 0
+    df["rare_action"] = 0
+
+    for idx, row in df.iterrows():
+        u = row["user"]
+        country = str(row.get("country", ""))
+        device = str(row.get("device", ""))
+        ip = str(row.get("ip", ""))
+        action = str(row.get("action", ""))
+        prof = profiles.get(u, {
+            "common_country": None,
+            "devices": [],
+            "ips": [],
+            "action_counts": {},
+            "total_actions": 0,
+        })
+        if prof.get("common_country") and country and country != prof.get("common_country"):
+            df.at[idx, "location_change"] = 1
+        if device and device not in set(prof.get("devices", [])):
+            df.at[idx, "new_device"] = 1
+        if ip and ip not in set(prof.get("ips", [])):
+            df.at[idx, "new_ip"] = 1
+        total = max(1, prof.get("total_actions", 0))
+        count = prof.get("action_counts", {}).get(action, 0)
+        rarity = count / total
+        if rarity <= 0.05:
+            df.at[idx, "rare_action"] = 1
+
+    # Impossible travel (simplified): location change with very short time gap
+    df["impossible_travel"] = df.apply(lambda r: 1 if (r["location_change"] == 1 and r["time_since_last_minutes"] < 120) else 0, axis=1)
+
+    # Keep only expected columns; fill NaNs
+    feature_df = df[["user", "timestamp"] + FEATURE_COLUMNS].fillna(0)
+    return feature_df
+
+
+# -----------------
+# Training & Scoring
+# -----------------
+
+def train_baseline(csv_file):
+    try:
+        df = pd.read_csv(csv_file)
+    except Exception:
+        # try excel
+        df = pd.read_excel(csv_file, engine="openpyxl")
+
+    # Validate schema
+    required_cols = {"user", "timestamp", "action", "success", "country", "device", "ip"}
+    missing = required_cols - set(df.columns)
+    if missing:
+        raise ValueError(f"Missing columns: {sorted(list(missing))}")
+
+    # Build profiles
+    profiles = build_baseline_profiles(df)
+    feature_df = extract_features(df, profiles)
+
+    # Fit scaler and model
+    X = feature_df[FEATURE_COLUMNS].astype(float).values
+    scaler = MinMaxScaler()
+    X_scaled = scaler.fit_transform(X)
+
+    iso = IsolationForest(
+        n_estimators=200,
+        contamination=0.02,  # assume ~2% anomalies in baseline
+        random_state=42,
+        n_jobs=-1
+    )
+    iso.fit(X_scaled)
+
+    # For scaling anomaly scores later
+    decision_scores = iso.decision_function(X_scaled)
+    # Lower decision_function -> more anomalous; we'll invert
+    anomaly_raw = -decision_scores
+    anom_min = float(np.min(anomaly_raw))
+    anom_max = float(np.max(anomaly_raw))
+
+    # Persist artifacts
+    with open(MODEL_PATH, "wb") as f:
+        pickle.dump(iso, f)
+    with open(SCALER_PATH, "wb") as f:
+        pickle.dump(scaler, f)
+    with open(PROFILE_PATH, "w") as f:
+        json.dump(profiles, f)
+    with open(ANOMALY_RANGE_PATH, "w") as f:
+        json.dump({"min": anom_min, "max": anom_max}, f)
+    with open(FEATURES_JSON, "w") as f:
+        json.dump(FEATURE_COLUMNS, f)
+
+    summary = {
+        "users": len(profiles),
+        "events": int(df.shape[0]),
+        "features_shape": list(X.shape),
+        "anomaly_range": {"min": anom_min, "max": anom_max},
+    }
+    return "Baseline trained ✅", pd.DataFrame(feature_df.head(10)), json.dumps(summary, indent=2)
+
+
+def _load_artifacts():
+    if not (os.path.exists(MODEL_PATH) and os.path.exists(SCALER_PATH) and os.path.exists(PROFILE_PATH) and os.path.exists(ANOMALY_RANGE_PATH)):
+        raise RuntimeError("Artifacts not found. Please train the baseline first.")
+    with open(MODEL_PATH, "rb") as f:
+        iso = pickle.load(f)
+    with open(SCALER_PATH, "rb") as f:
+        scaler = pickle.load(f)
+    with open(PROFILE_PATH, "r") as f:
+        profiles = json.load(f)
+    with open(ANOMALY_RANGE_PATH, "r") as f:
+        anomaly_range = json.load(f)
+    return iso, scaler, profiles, anomaly_range
+
+
+def _blend_risk(anomaly_raw, rule_risk):
+    # Normalize anomaly_raw to 0..100 using training range
+    with open(ANOMALY_RANGE_PATH, "r") as f:
+        rng = json.load(f)
+    mn, mx = rng["min"], rng["max"]
+    if mx <= mn:
+        anom_norm = 50.0
+    else:
+        anom_norm = 100.0 * (anomaly_raw - mn) / (mx - mn)
+        anom_norm = float(np.clip(anom_norm, 0, 100))
+    # Blend: 60% model, 40% rules
+    final = 0.6 * anom_norm + 0.4 * rule_risk
+    return float(np.clip(final, 0, 100)), float(anom_norm)
+
+
+def score_events(csv_file):
+    iso, scaler, profiles, _ = _load_artifacts()
+
+    try:
+        df = pd.read_csv(csv_file)
+    except Exception:
+        df = pd.read_excel(csv_file, engine="openpyxl")
+
+    required_cols = {"user", "timestamp", "action", "success", "country", "device", "ip"}
+    missing = required_cols - set(df.columns)
+    if missing:
+        raise ValueError(f"Missing columns: {sorted(list(missing))}")
+
+    feats = extract_features(df, profiles)
+    X = feats[FEATURE_COLUMNS].astype(float).values
+    X_scaled = scaler.transform(X)
+    decision_scores = iso.decision_function(X_scaled)
+    anomaly_raw = -decision_scores
+
+    # Compute rule risk and reasons
+    rule_risks = []
+    reasons = []
+    for idx, row in feats.iterrows():
+        rr = 0.0
+        rs = []
+        for k, w in RULE_WEIGHTS.items():
+            if row[k] == 1:
+                rr += w
+                rs.append(f"{k.replace('_', ' ').title()} (+{w})")
+        rr = float(np.clip(rr, 0, 100))
+        rule_risks.append(rr)
+        reasons.append("; ".join(rs) if rs else "None")
+
+    final_scores = []
+    anom_norms = []
+    for a, rr in zip(anomaly_raw, rule_risks):
+        final, anorm = _blend_risk(a, rr)
+        final_scores.append(final)
+        anom_norms.append(anorm)
+
+    out = pd.DataFrame({
+        "user": feats["user"],
+        "timestamp": feats["timestamp"],
+        "risk_score": final_scores,
+        "model_anomaly": anom_norms,
+        "rule_risk": rule_risks,
+        "reasons": reasons,
+        "failed_login": feats["failed_login"],
+        "is_night": feats["is_night"],
+        "location_change": feats["location_change"],
+        "new_device": feats["new_device"],
+        "new_ip": feats["new_ip"],
+        "rare_action": feats["rare_action"],
+        "impossible_travel": feats["impossible_travel"],
+    })
+
+    # Sort by highest risk first
+    out.sort_values("risk_score", ascending=False, inplace=True)
+    return out
+
+
+# -----------------
+# Gradio UI
+# -----------------
+
+def ui_train(file):
+    if file is None:
+        return "Please upload a CSV.", None, None
+    status, head_df, summary = train_baseline(file.name)
+    return status, head_df, summary
+
+
+def ui_score(file):
+    if file is None:
+        return None
+    out_df = score_events(file.name)
+    return out_df
+
+with gr.Blocks(title="UEBA Risk Scoring (Demo)") as demo:
+    gr.Markdown("""
+    # UEBA Risk Scoring (Demo)
+    Train an unsupervised anomaly detector on historical logs and score new events with a blended risk score.
+
+    **Note:** This demo is simplified for illustration; tailor features, weights, and thresholds to your environment.
+    """)
+
+    with gr.Tab("1) Train Baseline"):
+        gr.Markdown("Upload historical logs (CSV) to learn normal behavior.")
+        train_file = gr.File(file_types=[".csv", ".xlsx"], label="Training data")
+        train_btn = gr.Button("Train Baseline")
+        train_status = gr.Markdown()
+        train_head = gr.Dataframe(headers=None, interactive=False)
+        train_summary = gr.JSON()
+        train_btn.click(ui_train, inputs=[train_file], outputs=[train_status, train_head, train_summary])
+
+    with gr.Tab("2) Score Events"):
+        gr.Markdown("Upload new events (CSV) to get risk scores.")
+        score_file = gr.File(file_types=[".csv", ".xlsx"], label="Events to score")
+        score_btn = gr.Button("Score")
+        score_df = gr.Dataframe(interactive=False)
+        score_btn.click(ui_score, inputs=[score_file], outputs=[score_df])
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio==4.44.0
+pandas>=2.0.0
+numpy>=1.24.0
+scikit-learn>=1.3.0
+openpyxl>=3.1.0

sample_events.csv

@@ -0,0 +1,56 @@
+user,timestamp,action,success,country,device,ip
+alice,2025-09-09T17:00:00,login,1,US,alice_phone,10.0.0.112
+alice,2025-09-10T00:01:00,login,1,US,alice_laptop,10.0.0.125
+alice,2025-09-09T22:21:00,login,1,US,alice_phone,10.0.0.188
+alice,2025-09-10T01:34:00,login,1,US,alice_phone,10.0.0.32
+alice,2025-09-09T22:20:00,login,1,US,alice_phone,10.0.0.116
+alice,2025-09-09T21:14:00,login,1,US,alice_phone,10.0.0.116
+alice,2025-09-10T00:39:00,admin_change,1,US,alice_phone,10.0.0.113
+alice,2025-09-09T17:24:00,login,1,US,alice_phone,10.0.0.97
+alice,2025-09-10T01:51:00,file_download,1,US,alice_laptop,10.0.0.180
+alice,2025-09-09T22:24:00,login,1,US,alice_laptop,10.0.0.51
+alice,2025-09-09T10:55:00,login,0,NG,alice_unknown,172.16.0.74
+bob,2025-09-09T23:23:00,login,1,US,bob_laptop,10.0.1.85
+bob,2025-09-09T23:54:00,file_download,1,US,bob_laptop,10.0.1.176
+bob,2025-09-09T23:17:00,login,1,US,bob_phone,10.0.1.130
+bob,2025-09-09T22:15:00,view_report,1,US,bob_laptop,10.0.1.27
+bob,2025-09-09T17:42:00,admin_change,1,US,bob_laptop,10.0.1.220
+bob,2025-09-09T16:54:00,login,1,US,bob_phone,10.0.1.176
+bob,2025-09-09T20:03:00,file_download,1,US,bob_phone,10.0.1.246
+bob,2025-09-09T18:31:00,login,1,US,bob_laptop,10.0.1.16
+bob,2025-09-09T16:44:00,admin_change,1,US,bob_laptop,10.0.1.158
+bob,2025-09-09T17:03:00,admin_change,1,US,bob_phone,10.0.1.223
+bob,2025-09-09T10:54:00,login,0,NG,bob_unknown,172.16.1.27
+charlie,2025-09-09T20:24:00,login,0,DE,charlie_phone,10.0.2.162
+charlie,2025-09-10T01:04:00,view_report,1,DE,charlie_phone,10.0.2.221
+charlie,2025-09-10T00:51:00,view_report,1,DE,charlie_phone,10.0.2.211
+charlie,2025-09-10T00:10:00,view_report,1,DE,charlie_phone,10.0.2.108
+charlie,2025-09-10T00:20:00,login,1,DE,charlie_phone,10.0.2.220
+charlie,2025-09-10T01:47:00,file_download,1,DE,charlie_phone,10.0.2.218
+charlie,2025-09-09T16:20:00,login,1,DE,charlie_laptop,10.0.2.175
+charlie,2025-09-09T17:57:00,file_download,1,DE,charlie_laptop,10.0.2.176
+charlie,2025-09-09T21:16:00,view_report,1,DE,charlie_laptop,10.0.2.94
+charlie,2025-09-09T18:32:00,login,1,DE,charlie_phone,10.0.2.227
+charlie,2025-09-10T07:55:00,login,0,NG,charlie_unknown,172.16.2.47
+dana,2025-09-09T22:05:00,view_report,1,GB,dana_phone,10.0.3.1
+dana,2025-09-09T23:54:00,login,1,GB,dana_phone,10.0.3.186
+dana,2025-09-10T00:46:00,login,1,GB,dana_laptop,10.0.3.57
+dana,2025-09-09T20:52:00,file_download,1,GB,dana_phone,10.0.3.228
+dana,2025-09-09T16:38:00,view_report,1,GB,dana_phone,10.0.3.77
+dana,2025-09-09T17:50:00,view_report,1,GB,dana_laptop,10.0.3.219
+dana,2025-09-09T22:58:00,login,1,GB,dana_phone,10.0.3.142
+dana,2025-09-09T19:52:00,login,1,GB,dana_phone,10.0.3.78
+dana,2025-09-09T19:48:00,login,1,GB,dana_phone,10.0.3.102
+dana,2025-09-09T22:21:00,view_report,1,GB,dana_laptop,10.0.3.110
+dana,2025-09-09T10:02:00,login,0,CN,dana_unknown,172.16.3.19
+ed,2025-09-10T00:27:00,login,1,US,ed_laptop,10.0.4.73
+ed,2025-09-09T22:56:00,login,1,US,ed_phone,10.0.4.120
+ed,2025-09-09T21:52:00,login,1,US,ed_phone,10.0.4.9
+ed,2025-09-09T16:55:00,login,1,US,ed_laptop,10.0.4.227
+ed,2025-09-10T00:58:00,file_download,1,US,ed_laptop,10.0.4.196
+ed,2025-09-09T19:27:00,view_report,1,US,ed_laptop,10.0.4.125
+ed,2025-09-09T21:40:00,admin_change,1,US,ed_laptop,10.0.4.183
+ed,2025-09-09T16:47:00,login,1,US,ed_phone,10.0.4.238
+ed,2025-09-09T17:20:00,login,1,US,ed_phone,10.0.4.101
+ed,2025-09-09T18:43:00,login,1,US,ed_laptop,10.0.4.137
+ed,2025-09-10T07:34:00,login,0,RU,ed_unknown,172.16.4.226

sample_training.csv

@@ -0,0 +1,532 @@
+user,timestamp,action,success,country,device,ip
+alice,2025-09-02T03:28:00,login,1,US,alice_laptop,10.0.0.103
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