Create ai_monitor.py

ai_monitor.py

@@ -0,0 +1,21 @@
+# New ai_monitor.py
+from sklearn.ensemble import IsolationForest
+import numpy as np
+
+class AIActivityMonitor:
+    def __init__(self):
+        self.model = IsolationForest(contamination=0.1)
+        
+    def detect_unusual_activity(self, activity_data):
+        # Convert activity data to features
+        features = self._extract_features(activity_data)
+        predictions = self.model.fit_predict(features)
+        return predictions == -1  # True for anomalies
+
+    def _extract_features(self, activity_data):
+        # Extract numerical features from activity data
+        return np.array([[
+            data.get('login_count', 0),
+            data.get('post_count', 0),
+            data.get('interaction_count', 0)
+        ] for data in activity_data])