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)