File size: 2,833 Bytes
d2e47ba 812eea2 d2e47ba 812eea2 511e3f8 812eea2 511e3f8 812eea2 d2e47ba 787e83a d2e47ba 812eea2 d2e47ba faa66dd d2e47ba 812eea2 787e83a faa66dd 787e83a 812eea2 faa66dd 812eea2 faa66dd 812eea2 d2e47ba 812eea2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | import streamlit as st
import pandas as pd
import numpy as np
import joblib
from xgboost import XGBClassifier
# Load all models and preprocessors
model = joblib.load("best_xgboost_model.pkl")
label_encoder = joblib.load("label_encoder.pkl")
scaler = joblib.load("standard_scaler.pkl")
var_thresh = joblib.load("variance_threshold.pkl")
mi_features = joblib.load("selected_mi_features.pkl")
roa_features = joblib.load("selected_roa_features.pkl")
class_names = joblib.load("class_names.pkl")
FEATURE_COLUMNS = [
"flow_duration", "header_length", "protocol_type", "duration", "rate", "srate", "drate",
"fin_flag_number", "syn_flag_number", "rst_flag_number", "psh_flag_number", "ack_flag_number",
"ece_flag_number", "cwr_flag_number", "ack_count", "syn_count", "fin_count", "urg_count",
"rst_count", "http", "https", "dns", "telnet", "smtp", "ssh", "irc", "tcp", "udp", "dhcp",
"arp", "icmp", "ipv", "llc", "tot_sum", "min", "max", "avg", "std", "tot_size", "iat",
"number", "magnitue", "radius", "covariance", "variance", "weight"
]
# Streamlit UI
st.title("🔐 Detection of DDoS Attack in IoT Networks")
st.markdown("Paste a single row of IoT traffic data for DDoS detection (comma **or** tab-separated).")
default_row = (
"0,54,6,64,0.329807153,0.329807153,0,1,0,1,0,0,0,0,1,0,1,0,0,"
"0,0,0,0,0,0,0,1,0,0,0,0,1,1,567,54,54,54,0,54,83343831.92,"
"9.5,10.39230485,0,0,0,141.55"
)
user_input = st.text_area("Paste the feature row here (label column optional)", default_row, height=150)
if st.button("🔍 Predict Attack Type"):
try:
delimiter = '\t' if '\t' in user_input else ','
input_parts = user_input.strip().split(delimiter)
# Handle optional label at the end
try:
_ = float(input_parts[-1]) # If this fails, it's a string label
except ValueError:
input_parts = input_parts[:-1] # Drop the label
input_list = [float(x.strip()) for x in input_parts]
if len(input_list) != len(FEATURE_COLUMNS):
st.error(f"🚫 Expected {len(FEATURE_COLUMNS)} features but got {len(input_list)}.")
else:
input_df = pd.DataFrame([input_list], columns=FEATURE_COLUMNS)
# Preprocessing pipeline
scaled = scaler.transform(input_df)
var_filtered = var_thresh.transform(scaled)
mi_selected = pd.DataFrame(var_filtered, columns=np.array(FEATURE_COLUMNS)[var_thresh.get_support()])
final_features = mi_selected[mi_features].iloc[:, roa_features]
prediction = model.predict(final_features)
predicted_label = label_encoder.inverse_transform(prediction)[0]
st.success(f"✅ Predicted Attack Type: **{predicted_label}**")
except Exception as e:
st.error(f"🚫 Error: {e}")
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