From 82e30e5af68a5a66009a013a48bbda22d9509817 Mon Sep 17 00:00:00 2001 From: KOSASIH Date: Sun, 15 Sep 2024 13:58:28 +0700 Subject: [PATCH] Create real_time_threat_intelligence.py --- .../real_time_threat_intelligence.py | 75 +++++++++++++++++++ 1 file changed, 75 insertions(+) create mode 100644 advanced_feature/real_time_threat_intelligence/real_time_threat_intelligence.py diff --git a/advanced_feature/real_time_threat_intelligence/real_time_threat_intelligence.py b/advanced_feature/real_time_threat_intelligence/real_time_threat_intelligence.py new file mode 100644 index 000000000..bb7d1a1aa --- /dev/null +++ b/advanced_feature/real_time_threat_intelligence/real_time_threat_intelligence.py @@ -0,0 +1,75 @@ +import os +import sys +import time +import logging +import requests +from flask import Flask, request, jsonify +from flask_socketio import SocketIO, emit +from sklearn.ensemble import RandomForestClassifier +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.metrics import accuracy_score +from sklearn.model_selection import train_test_split + +app = Flask(__name__) +app.config['SECRET_KEY'] = 'secret!' +socketio = SocketIO(app, cors_allowed_origins='*') + +class RealTimeThreatIntelligence: + def __init__(self): + self.logger = logging.getLogger('RealTimeThreatIntelligence') + self.classifier = RandomForestClassifier(n_estimators=100) + self.vectorizer = TfidfVectorizer() + self.threat_database = {} + + def train_model(self): + # Train the threat intelligence model using historical threat data + threat_data = self.load_threat_data() + X_train, X_test, y_train, y_test = train_test_split(threat_data['features'], threat_data['labels'], test_size=0.2, random_state=42) + X_train_vectors = self.vectorizer.fit_transform(X_train) + self.classifier.fit(X_train_vectors, y_train) + y_pred = self.classifier.predict(self.vectorizer.transform(X_test)) + accuracy = accuracy_score(y_test, y_pred) + self.logger.info(f'Threat intelligence model trained with accuracy: {accuracy:.3f}') + + def load_threat_data(self): + # Load historical threat data from database or API + # This implementation is highly simplified and may not be suitable for production use + threat_data = { + 'features': ['malicious IP address', 'suspicious DNS query', 'unusual network traffic'], + 'labels': [1, 1, 0] + } + return threat_data + + def analyze_network_traffic(self, traffic_data): + # Analyze network traffic in real-time using the trained threat intelligence model + traffic_vectors = self.vectorizer.transform([traffic_data]) + prediction = self.classifier.predict(traffic_vectors) + if prediction[0] == 1: + self.logger.warning(f'Potential threat detected: {traffic_data}') + return True + else: + return False + + def respond_to_threat(self, threat_data): + # Respond to detected threats in real-time using automated incident response + # This implementation is highly simplified and may not be suitable for production use + self.logger.info(f'Responding to threat: {threat_data}') + # Implement automated incident response actions here + +@socketio.on('connect') +def connect(): + self.logger.info('Client connected') + +@socketio.on('disconnect') +def disconnect(): + self.logger.info('Client disconnected') + +@socketio.on('network_traffic') +def handle_network_traffic(traffic_data): + rtti = RealTimeThreatIntelligence() + if rtti.analyze_network_traffic(traffic_data): + rtti.respond_to_threat(traffic_data) + emit('threat_response', {'message': 'Threat response sent'}) + +if __name__ == '__main__': + socketio.run(app, host='0.0.0.0', port=5000)