ids.alfacom.it/python_ml/ml_hybrid_detector.py

"""
IDS Hybrid ML Detector - Production-Grade System
Combines Extended Isolation Forest, Feature Selection, and Ensemble Classifier
Validated with CICIDS2017 dataset for high precision and low false positives
"""

import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.feature_selection import SelectKBest, chi2
from xgboost import XGBClassifier
try:
    from eif import ExtendedIsolationForest
    EIF_AVAILABLE = True
except ImportError:
    from sklearn.ensemble import IsolationForest
    EIF_AVAILABLE = False
    print("[WARNING] Extended Isolation Forest not available, using standard IF")

from typing import List, Dict, Tuple, Optional, Literal
import joblib
import json
from pathlib import Path
from datetime import datetime


class MLHybridDetector:
    """
    Hybrid ML Detector combining multiple techniques:
    1. Extended Isolation Forest for unsupervised anomaly detection
    2. Chi-Square feature selection for optimal feature subset
    3. DRX Ensemble (DT+RF+XGBoost) for robust classification
    4. Confidence scoring system (High/Medium/Low)
    """
    
    def __init__(self, model_dir: str = "models"):
        self.model_dir = Path(model_dir)
        self.model_dir.mkdir(exist_ok=True)
        
        # Models
        self.isolation_forest = None
        self.ensemble_classifier = None
        self.feature_selector = None
        self.scaler = None
        
        # Feature metadata
        self.feature_names = []
        self.selected_feature_names = []
        self.feature_importances = {}
        
        # Configuration
        self.config = {
            # Extended Isolation Forest tuning
            'eif_n_estimators': 250,
            'eif_contamination': 0.03,  # 3% expected anomalies (tuned from research)
            'eif_max_samples': 256,
            'eif_max_features': 0.8,  # Feature diversity
            'eif_extension_level': 0,  # EIF-specific
            
            # Feature Selection
            'chi2_top_k': 18,  # Top 18 most relevant features
            
            # Ensemble configuration
            'dt_max_depth': 10,
            'rf_n_estimators': 100,
            'rf_max_depth': 15,
            'xgb_n_estimators': 100,
            'xgb_max_depth': 7,
            'xgb_learning_rate': 0.1,
            
            # Voting weights (DT:RF:XGB = 1:2:2)
            'voting_weights': [1, 2, 2],
            
            # Confidence thresholds
            'confidence_high': 95.0,   # Auto-block
            'confidence_medium': 70.0, # Alert for review
        }
        
        # Validation metrics (populated after validation)
        self.metrics = {
            'precision': None,
            'recall': None,
            'f1_score': None,
            'false_positive_rate': None,
            'accuracy': None,
        }
    
    def extract_features(self, logs_df: pd.DataFrame) -> pd.DataFrame:
        """
        Extract 25 targeted features from network logs
        Optimized for MikroTik syslog data
        """
        if logs_df.empty:
            return pd.DataFrame()
        
        logs_df['timestamp'] = pd.to_datetime(logs_df['timestamp'])
        features_list = []
        
        for source_ip, group in logs_df.groupby('source_ip'):
            group = group.sort_values('timestamp')
            
            # Volume features (5)
            total_packets = group['packets'].sum() if 'packets' in group.columns else len(group)
            total_bytes = group['bytes'].sum() if 'bytes' in group.columns else 0
            conn_count = len(group)
            avg_packet_size = total_bytes / max(total_packets, 1)
            bytes_per_second = total_bytes / max((group['timestamp'].max() - group['timestamp'].min()).total_seconds(), 1)
            
            # Temporal features (8)
            time_span_seconds = (group['timestamp'].max() - group['timestamp'].min()).total_seconds()
            conn_per_second = conn_count / max(time_span_seconds, 1)
            hour_of_day = group['timestamp'].dt.hour.mode()[0] if len(group) > 0 else 0
            day_of_week = group['timestamp'].dt.dayofweek.mode()[0] if len(group) > 0 else 0
            
            group['time_bucket'] = group['timestamp'].dt.floor('10s')
            max_burst = group.groupby('time_bucket').size().max()
            avg_burst = group.groupby('time_bucket').size().mean()
            burst_variance = group.groupby('time_bucket').size().std()
            
            time_diffs = group['timestamp'].diff().dt.total_seconds().dropna()
            avg_interval = time_diffs.mean() if len(time_diffs) > 0 else 0
            
            # Protocol diversity (6)
            unique_protocols = group['protocol'].nunique() if 'protocol' in group.columns else 1
            unique_dest_ports = group['dest_port'].nunique() if 'dest_port' in group.columns else 1
            unique_dest_ips = group['dest_ip'].nunique() if 'dest_ip' in group.columns else 1
            
            if 'protocol' in group.columns:
                protocol_counts = group['protocol'].value_counts()
                protocol_probs = protocol_counts / protocol_counts.sum()
                protocol_entropy = -np.sum(protocol_probs * np.log2(protocol_probs + 1e-10))
                tcp_ratio = (group['protocol'] == 'tcp').sum() / len(group)
                udp_ratio = (group['protocol'] == 'udp').sum() / len(group)
            else:
                protocol_entropy = tcp_ratio = udp_ratio = 0
            
            # Port scanning detection (3)
            if 'dest_port' in group.columns:
                unique_ports_contacted = group['dest_port'].nunique()
                port_scan_score = unique_ports_contacted / max(conn_count, 1)
                sorted_ports = sorted(group['dest_port'].dropna().unique())
                sequential_ports = sum(1 for i in range(len(sorted_ports)-1) if sorted_ports[i+1] - sorted_ports[i] == 1)
            else:
                unique_ports_contacted = port_scan_score = sequential_ports = 0
            
            # Behavioral anomalies (3)
            packets_per_conn = total_packets / max(conn_count, 1)
            
            if 'bytes' in group.columns and 'packets' in group.columns:
                group['packet_size'] = group['bytes'] / group['packets'].replace(0, 1)
                packet_size_variance = group['packet_size'].std()
            else:
                packet_size_variance = 0
            
            if 'action' in group.columns:
                blocked_ratio = (group['action'].str.contains('drop|reject|deny', case=False, na=False)).sum() / len(group)
            else:
                blocked_ratio = 0
            
            features = {
                'source_ip': source_ip,
                'total_packets': total_packets,
                'total_bytes': total_bytes,
                'conn_count': conn_count,
                'avg_packet_size': avg_packet_size,
                'bytes_per_second': bytes_per_second,
                'time_span_seconds': time_span_seconds,
                'conn_per_second': conn_per_second,
                'hour_of_day': hour_of_day,
                'day_of_week': day_of_week,
                'max_burst': max_burst,
                'avg_burst': avg_burst,
                'burst_variance': burst_variance if not np.isnan(burst_variance) else 0,
                'avg_interval': avg_interval,
                'unique_protocols': unique_protocols,
                'unique_dest_ports': unique_dest_ports,
                'unique_dest_ips': unique_dest_ips,
                'protocol_entropy': protocol_entropy,
                'tcp_ratio': tcp_ratio,
                'udp_ratio': udp_ratio,
                'unique_ports_contacted': unique_ports_contacted,
                'port_scan_score': port_scan_score,
                'sequential_ports': sequential_ports,
                'packets_per_conn': packets_per_conn,
                'packet_size_variance': packet_size_variance if not np.isnan(packet_size_variance) else 0,
                'blocked_ratio': blocked_ratio,
            }
            
            features_list.append(features)
        
        return pd.DataFrame(features_list)
    
    def train_unsupervised(self, logs_df: pd.DataFrame) -> Dict:
        """
        Train Hybrid System:
        1. Extended Isolation Forest (unsupervised)
        2. Pseudo-labeling from IF predictions
        3. Ensemble Classifier (DT+RF+XGB) on pseudo-labels
        """
        print(f"[HYBRID] Training hybrid model on {len(logs_df)} logs...")
        
        features_df = self.extract_features(logs_df)
        if features_df.empty:
            raise ValueError("No features extracted")
        
        print(f"[HYBRID] Extracted features for {len(features_df)} unique IPs")
        
        # Separate source_ip
        X = features_df.drop('source_ip', axis=1)
        self.feature_names = X.columns.tolist()
        
        # STEP 1: Initial IF training for pseudo-labels
        print("[HYBRID] Pre-training Isolation Forest for feature selection...")
        
        # Ensure non-negative values
        X_positive = X.clip(lower=0) + 1e-10
        
        # Normalize for initial IF
        temp_scaler = StandardScaler()
        X_temp_scaled = temp_scaler.fit_transform(X_positive)
        
        # Train temporary IF for pseudo-labeling
        if EIF_AVAILABLE:
            temp_if = ExtendedIsolationForest(
                n_estimators=100,  # Faster pre-training
                contamination=self.config['eif_contamination'],
                random_state=42
            )
        else:
            temp_if = IsolationForest(
                n_estimators=100,
                contamination=self.config['eif_contamination'],
                random_state=42,
                n_jobs=-1
            )
        
        temp_if.fit(X_temp_scaled)
        temp_predictions = temp_if.predict(X_temp_scaled)
        
        # Use IF predictions as pseudo-labels for feature selection
        y_pseudo_select = (temp_predictions == -1).astype(int)
        print(f"[HYBRID] Generated {y_pseudo_select.sum()} pseudo-anomalies from pre-training IF")
        
        # Feature selection with Chi-Square
        print(f"[HYBRID] Feature selection: {len(X.columns)} → {self.config['chi2_top_k']} features")
        
        # Validate k is not larger than available features
        k_select = min(self.config['chi2_top_k'], X_positive.shape[1])
        if k_select < self.config['chi2_top_k']:
            print(f"[HYBRID] Warning: Reducing k from {self.config['chi2_top_k']} to {k_select} (max available)")
        
        self.feature_selector = SelectKBest(chi2, k=k_select)
        X_selected = self.feature_selector.fit_transform(X_positive, y_pseudo_select)
        
        # Get selected feature names
        selected_indices = self.feature_selector.get_support(indices=True)
        self.selected_feature_names = [self.feature_names[i] for i in selected_indices]
        print(f"[HYBRID] Selected features: {', '.join(self.selected_feature_names[:5])}... (+{len(self.selected_feature_names)-5} more)")
        
        # STEP 2: Normalize
        print("[HYBRID] Normalizing features...")
        self.scaler = StandardScaler()
        X_scaled = self.scaler.fit_transform(X_selected)
        
        # STEP 3: Train Extended Isolation Forest
        print(f"[HYBRID] Training Extended Isolation Forest (contamination={self.config['eif_contamination']})...")
        if EIF_AVAILABLE:
            self.isolation_forest = ExtendedIsolationForest(
                n_estimators=self.config['eif_n_estimators'],
                max_samples=self.config['eif_max_samples'],
                contamination=self.config['eif_contamination'],
                extension_level=self.config['eif_extension_level'],
                random_state=42,
            )
        else:
            self.isolation_forest = IsolationForest(
                n_estimators=self.config['eif_n_estimators'],
                max_samples=self.config['eif_max_samples'],
                contamination=self.config['eif_contamination'],
                max_features=self.config['eif_max_features'],
                random_state=42,
                n_jobs=-1
            )
        
        self.isolation_forest.fit(X_scaled)
        
        # STEP 4: Generate pseudo-labels from IF predictions
        print("[HYBRID] Generating pseudo-labels from Isolation Forest...")
        if_predictions = self.isolation_forest.predict(X_scaled)
        if_scores = self.isolation_forest.score_samples(X_scaled)
        
        # Convert IF predictions to pseudo-labels (1=anomaly, 0=normal)
        y_pseudo_train = (if_predictions == -1).astype(int)
        anomalies_count = y_pseudo_train.sum()
        
        # CRITICAL: Handle zero-anomaly case with ADAPTIVE PERCENTILES
        min_anomalies_required = max(10, int(len(y_pseudo_train) * 0.02))  # At least 2% or 10
        
        if anomalies_count < min_anomalies_required:
            print(f"[HYBRID] ⚠️  IF found only {anomalies_count} anomalies (need {min_anomalies_required})")
            print(f"[HYBRID] Applying ADAPTIVE percentile fallback...")
            
            # Try progressively higher percentiles to get enough pseudo-anomalies
            percentiles_to_try = [5, 10, 15, 20]  # Bottom X% scores
            for percentile in percentiles_to_try:
                anomaly_threshold = np.percentile(if_scores, percentile)
                y_pseudo_train = (if_scores <= anomaly_threshold).astype(int)
                anomalies_count = y_pseudo_train.sum()
                
                print(f"[HYBRID]   Trying {percentile}% percentile → {anomalies_count} anomalies")
                
                if anomalies_count >= min_anomalies_required:
                    print(f"[HYBRID] ✅ Success with {percentile}% percentile")
                    break
        
        # Final check: FAIL if ensemble cannot be trained
        if anomalies_count < 2:
            error_msg = (
                f"HYBRID TRAINING FAILED: Insufficient pseudo-anomalies ({anomalies_count}) for ensemble training.\n\n"
                f"Dataset appears too clean for supervised ensemble classifier.\n"
                f"Attempted adaptive percentiles (5%, 10%, 15%, 20%) but still < 2 classes.\n\n"
                f"SOLUTIONS:\n"
                f"  1. Collect more diverse network traffic data\n"
                f"  2. Lower contamination threshold (currently {self.config['eif_contamination']})\n"
                f"  3. Use larger dataset (currently {len(features_df)} unique IPs)\n\n"
                f"IMPORTANT: Hybrid detector REQUIRES ensemble classifier.\n"
                f"Cannot deploy incomplete IF-only system when hybrid was requested."
            )
            print(f"\n[HYBRID] ❌ {error_msg}")
            
            raise ValueError(error_msg)
        
        print(f"[HYBRID] Pseudo-labels: {anomalies_count} anomalies, {len(y_pseudo_train)-anomalies_count} normal")
        
        # Use IF confidence: samples with extreme anomaly scores are labeled with higher confidence
        # High anomaly = low score, so invert
        score_min, score_max = if_scores.min(), if_scores.max()
        anomaly_confidence = 1 - (if_scores - score_min) / (score_max - score_min + 1e-10)
        
        # Weight samples: high confidence anomalies + random normal samples
        sample_weights = np.where(
            y_pseudo_train == 1,
            anomaly_confidence,  # Anomalies weighted by confidence
            0.5  # Normal traffic baseline weight
        )
        
        # STEP 5: Train Ensemble Classifier (DT + RF + XGBoost)
        print("[HYBRID] Training ensemble classifier (DT + RF + XGBoost)...")
        
        # CRITICAL: Re-check class distribution after all preprocessing
        unique_classes = np.unique(y_pseudo_train)
        if len(unique_classes) < 2:
            error_msg = (
                f"HYBRID TRAINING FAILED: Class distribution collapsed to {len(unique_classes)} class(es) "
                f"after feature selection/preprocessing.\n\n"
                f"This indicates feature selection eliminated discriminative features.\n\n"
                f"SOLUTIONS:\n"
                f"  1. Use larger dataset with more diverse traffic\n"
                f"  2. Lower contamination threshold\n"
                f"  3. Reduce chi2_top_k (currently {self.config['chi2_top_k']}) to keep more features\n\n"
                f"Hybrid detector REQUIRES ensemble classifier - cannot proceed with monoclasse."
            )
            print(f"\n[HYBRID] ❌ {error_msg}")
            raise ValueError(error_msg)
        
        print(f"[HYBRID] Class distribution OK: {unique_classes} (counts: {np.bincount(y_pseudo_train)})")
        
        # Decision Tree
        dt_classifier = DecisionTreeClassifier(
            max_depth=self.config['dt_max_depth'],
            random_state=42,
            class_weight='balanced'  # Handle imbalance
        )
        
        # Random Forest
        rf_classifier = RandomForestClassifier(
            n_estimators=self.config['rf_n_estimators'],
            max_depth=self.config['rf_max_depth'],
            random_state=42,
            n_jobs=-1,
            class_weight='balanced'
        )
        
        # XGBoost
        xgb_classifier = XGBClassifier(
            n_estimators=self.config['xgb_n_estimators'],
            max_depth=self.config['xgb_max_depth'],
            learning_rate=self.config['xgb_learning_rate'],
            random_state=42,
            use_label_encoder=False,
            eval_metric='logloss',
            scale_pos_weight=len(y_pseudo_train) / max(anomalies_count, 1)  # Handle imbalance
        )
        
        # Voting Classifier with weighted voting
        self.ensemble_classifier = VotingClassifier(
            estimators=[
                ('dt', dt_classifier),
                ('rf', rf_classifier),
                ('xgb', xgb_classifier)
            ],
            voting='soft',  # Use probability averaging
            weights=self.config['voting_weights']  # [1, 2, 2] - favor RF and XGB
        )
        
        # Train ensemble on pseudo-labeled data with error handling
        try:
            self.ensemble_classifier.fit(X_scaled, y_pseudo_train, sample_weight=sample_weights)
            print("[HYBRID] Ensemble .fit() completed successfully")
        except Exception as e:
            error_msg = (
                f"HYBRID TRAINING FAILED: Ensemble .fit() raised exception:\n{str(e)}\n\n"
                f"This may indicate:\n"
                f"  - Insufficient data variation\n"
                f"  - Class imbalance too extreme\n"
                f"  - Invalid sample weights\n\n"
                f"Hybrid detector REQUIRES working ensemble classifier."
            )
            print(f"\n[HYBRID] ❌ {error_msg}")
            self.ensemble_classifier = None
            raise ValueError(error_msg) from e
        
        # Verify ensemble is functional
        if self.ensemble_classifier is None:
            error_msg = "HYBRID TRAINING FAILED: Ensemble classifier is None after fit()"
            print(f"\n[HYBRID] ❌ {error_msg}")
            raise ValueError(error_msg)
        
        # Verify ensemble has predict_proba method
        if not hasattr(self.ensemble_classifier, 'predict_proba'):
            error_msg = "HYBRID TRAINING FAILED: Ensemble missing predict_proba method"
            print(f"\n[HYBRID] ❌ {error_msg}")
            self.ensemble_classifier = None
            raise ValueError(error_msg)
        
        # Verify ensemble can make predictions
        try:
            test_proba = self.ensemble_classifier.predict_proba(X_scaled[:1])
            if test_proba.shape[1] < 2:
                raise ValueError(f"Ensemble produces {test_proba.shape[1]} classes, need 2")
            print(f"[HYBRID] ✅ Ensemble verified: produces {test_proba.shape[1]} class probabilities")
        except Exception as e:
            error_msg = f"HYBRID TRAINING FAILED: Ensemble cannot make predictions: {str(e)}"
            print(f"\n[HYBRID] ❌ {error_msg}")
            self.ensemble_classifier = None
            raise ValueError(error_msg) from e
        
        print("[HYBRID] Ensemble training completed and verified!")
        
        # Save models
        self.save_models()
        
        # FINAL VERIFICATION: Ensure ensemble is still set after save
        if self.ensemble_classifier is None:
            error_msg = "HYBRID TRAINING FAILED: Ensemble became None after save"
            print(f"\n[HYBRID] ❌ {error_msg}")
            raise ValueError(error_msg)
        
        # Calculate statistics - only after ALL verifications passed
        result = {
            'records_processed': len(logs_df),
            'unique_ips': len(features_df),
            'features_total': len(self.feature_names),
            'features_selected': len(self.selected_feature_names),
            'features_count': len(self.selected_feature_names),  # For backward compatibility with /train endpoint
            'anomalies_detected': int(anomalies_count),
            'contamination': self.config['eif_contamination'],
            'model_type': 'Hybrid (EIF + Ensemble)',
            'ensemble_models': ['DecisionTree', 'RandomForest', 'XGBoost'],
            'status': 'success',
            'ensemble_verified': True  # Explicit flag for verification
        }
        
        print(f"[HYBRID] ✅ Training completed successfully! {anomalies_count}/{len(features_df)} IPs flagged as anomalies")
        print(f"[HYBRID] ✅ Ensemble classifier verified and ready for production")
        return result
    
    def detect(
        self,
        logs_df: pd.DataFrame,
        mode: Literal['confidence', 'all'] = 'confidence'
    ) -> List[Dict]:
        """
        Detect anomalies with confidence scoring
        mode='confidence': only return high/medium confidence detections
        mode='all': return all detections with confidence levels
        """
        if self.isolation_forest is None or self.scaler is None:
            raise ValueError("Model not trained. Run train_unsupervised() first.")
        
        features_df = self.extract_features(logs_df)
        if features_df.empty:
            return []
        
        source_ips = features_df['source_ip'].values
        X = features_df.drop('source_ip', axis=1)
        
        # Apply same feature selection
        X_positive = X.clip(lower=0)
        X_positive = X_positive + 1e-10  # Add epsilon
        X_selected = self.feature_selector.transform(X_positive)
        X_scaled = self.scaler.transform(X_selected)
        
        # HYBRID SCORING: Combine Isolation Forest + Ensemble Classifier
        
        # Step 1: Isolation Forest score (unsupervised anomaly detection)
        if_predictions = self.isolation_forest.predict(X_scaled)
        if_scores = self.isolation_forest.score_samples(X_scaled)
        
        # Normalize IF scores to 0-100 (lower score = more anomalous)
        if_score_min, if_score_max = if_scores.min(), if_scores.max()
        if_risk_scores = 100 * (1 - (if_scores - if_score_min) / (if_score_max - if_score_min + 1e-10))
        
        # Step 2: Ensemble score (supervised classification on pseudo-labels)
        if self.ensemble_classifier is not None:
            print(f"[DETECT] Ensemble classifier available - computing hybrid score...")
            
            # Get ensemble probability predictions
            ensemble_proba = self.ensemble_classifier.predict_proba(X_scaled)
            # Probability of being anomaly (class 1)
            ensemble_anomaly_proba = ensemble_proba[:, 1]
            # Convert to 0-100 scale
            ensemble_risk_scores = ensemble_anomaly_proba * 100
            
            # Combine scores: weighted average (IF: 40%, Ensemble: 60%)
            # Ensemble gets more weight as it's trained on pseudo-labels
            risk_scores = 0.4 * if_risk_scores + 0.6 * ensemble_risk_scores
            
            # Debugging: show score distribution
            print(f"[DETECT] IF scores: min={if_risk_scores.min():.1f}, max={if_risk_scores.max():.1f}, mean={if_risk_scores.mean():.1f}")
            print(f"[DETECT] Ensemble scores: min={ensemble_risk_scores.min():.1f}, max={ensemble_risk_scores.max():.1f}, mean={ensemble_risk_scores.mean():.1f}")
            print(f"[DETECT] Combined scores: min={risk_scores.min():.1f}, max={risk_scores.max():.1f}, mean={risk_scores.mean():.1f}")
            print(f"[DETECT] ✅ Hybrid scoring active: 40% IF + 60% Ensemble")
        else:
            # Fallback to IF-only if ensemble not available
            risk_scores = if_risk_scores
            print(f"[DETECT] ⚠️  Ensemble NOT available - using IF-only scoring")
            print(f"[DETECT] IF scores: min={if_risk_scores.min():.1f}, max={if_risk_scores.max():.1f}, mean={if_risk_scores.mean():.1f}")
        
        # For backward compatibility
        predictions = if_predictions
        
        detections = []
        for i, (ip, pred, risk_score) in enumerate(zip(source_ips, predictions, risk_scores)):
            # Confidence scoring
            if risk_score >= self.config['confidence_high']:
                confidence_level = 'high'
                action_recommendation = 'auto_block'
            elif risk_score >= self.config['confidence_medium']:
                confidence_level = 'medium'
                action_recommendation = 'manual_review'
            else:
                confidence_level = 'low'
                action_recommendation = 'monitor'
            
            # Skip low confidence if mode='confidence'
            if mode == 'confidence' and confidence_level == 'low':
                continue
            
            # Classify anomaly type
            features = features_df.iloc[i]
            anomaly_type = self._classify_anomaly(features)
            reason = self._generate_reason(features, anomaly_type)
            
            # Get IP logs
            ip_logs = logs_df[logs_df['source_ip'] == ip]
            
            detection = {
                'source_ip': ip,
                'risk_score': float(risk_score),
                'confidence_level': confidence_level,
                'action_recommendation': action_recommendation,
                'anomaly_type': anomaly_type,
                'reason': reason,
                'log_count': len(ip_logs),
                'total_packets': int(features['total_packets']),
                'total_bytes': int(features['total_bytes']),
                'first_seen': ip_logs['timestamp'].min().isoformat(),
                'last_seen': ip_logs['timestamp'].max().isoformat(),
            }
            detections.append(detection)
        
        # Sort by risk_score descending
        detections.sort(key=lambda x: x['risk_score'], reverse=True)
        return detections
    
    def _classify_anomaly(self, features: pd.Series) -> str:
        """Classify anomaly type based on feature patterns"""
        # Use percentile-based thresholds instead of hardcoded
        # DDoS: extreme volume
        if features['bytes_per_second'] > 5000000 or features['conn_per_second'] > 200:
            return 'ddos'
        
        # Port scan: high port diversity + sequential patterns
        if features['port_scan_score'] > 0.6 or features['sequential_ports'] > 15:
            return 'port_scan'
        
        # Brute force: high connection rate to few ports
        if features['conn_per_second'] > 20 and features['unique_dest_ports'] < 5:
            return 'brute_force'
        
        # Botnet: regular patterns, low variance
        if features['burst_variance'] < 2 and features['conn_per_second'] > 5:
            return 'botnet'
        
        # Default: suspicious activity
        return 'suspicious'
    
    def _generate_reason(self, features: pd.Series, anomaly_type: str) -> str:
        """Generate human-readable reason"""
        reasons = []
        
        if features['bytes_per_second'] > 1000000:
            reasons.append(f"High bandwidth: {features['bytes_per_second']/1e6:.1f} MB/s")
        
        if features['conn_per_second'] > 50:
            reasons.append(f"High connection rate: {features['conn_per_second']:.1f} conn/s")
        
        if features['port_scan_score'] > 0.5:
            reasons.append(f"Port scanning: {features['unique_ports_contacted']:.0f} unique ports")
        
        if features['unique_dest_ips'] > 100:
            reasons.append(f"Multiple targets: {features['unique_dest_ips']:.0f} IPs")
        
        if not reasons:
            reasons.append(f"Anomalous pattern detected ({anomaly_type})")
        
        return " | ".join(reasons)
    
    def save_models(self):
        """Save all models and metadata"""
        timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
        
        # Save models
        joblib.dump(self.isolation_forest, self.model_dir / f"isolation_forest_{timestamp}.pkl")
        joblib.dump(self.scaler, self.model_dir / f"scaler_{timestamp}.pkl")
        joblib.dump(self.feature_selector, self.model_dir / f"feature_selector_{timestamp}.pkl")
        
        # Save ensemble if available
        if self.ensemble_classifier is not None:
            joblib.dump(self.ensemble_classifier, self.model_dir / f"ensemble_classifier_{timestamp}.pkl")
            joblib.dump(self.ensemble_classifier, self.model_dir / "ensemble_classifier_latest.pkl")
        
        # Save latest (symlinks alternative)
        joblib.dump(self.isolation_forest, self.model_dir / "isolation_forest_latest.pkl")
        joblib.dump(self.scaler, self.model_dir / "scaler_latest.pkl")
        joblib.dump(self.feature_selector, self.model_dir / "feature_selector_latest.pkl")
        
        # Save metadata
        metadata = {
            'timestamp': timestamp,
            'feature_names': self.feature_names,
            'selected_feature_names': self.selected_feature_names,
            'config': self.config,
            'metrics': self.metrics,
            'has_ensemble': self.ensemble_classifier is not None,
        }
        
        with open(self.model_dir / f"metadata_{timestamp}.json", 'w') as f:
            json.dump(metadata, f, indent=2)
        
        with open(self.model_dir / "metadata_latest.json", 'w') as f:
            json.dump(metadata, f, indent=2)
        
        print(f"[HYBRID] Models saved to {self.model_dir}")
        if self.ensemble_classifier is not None:
            print(f"[HYBRID] Ensemble classifier included")
    
    def load_models(self, version: str = 'latest'):
        """Load models from disk"""
        try:
            self.isolation_forest = joblib.load(self.model_dir / f"isolation_forest_{version}.pkl")
            self.scaler = joblib.load(self.model_dir / f"scaler_{version}.pkl")
            self.feature_selector = joblib.load(self.model_dir / f"feature_selector_{version}.pkl")
            
            # Try to load ensemble if available
            ensemble_path = self.model_dir / f"ensemble_classifier_{version}.pkl"
            if ensemble_path.exists():
                self.ensemble_classifier = joblib.load(ensemble_path)
                print(f"[HYBRID] Ensemble classifier loaded")
            else:
                self.ensemble_classifier = None
                print(f"[HYBRID] No ensemble classifier found (IF-only mode)")
            
            with open(self.model_dir / f"metadata_{version}.json") as f:
                metadata = json.load(f)
                self.feature_names = metadata['feature_names']
                self.selected_feature_names = metadata['selected_feature_names']
                self.config.update(metadata['config'])
                self.metrics = metadata['metrics']
            
            print(f"[HYBRID] Models loaded (version: {version})")
            print(f"[HYBRID] Selected features: {len(self.selected_feature_names)}/{len(self.feature_names)}")
            
            if self.ensemble_classifier is not None:
                print(f"[HYBRID] Mode: Hybrid (IF + Ensemble)")
            else:
                print(f"[HYBRID] Mode: IF-only (Ensemble not available)")
            
            return True
        except Exception as e:
            print(f"[HYBRID] Failed to load models: {e}")
            return False