ai_monitor.py

# 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])