How to Detect AI-Generated Cyber Attacks in 2026 (Beginne...

#!/usr/bin/env python3
"""
AI-Generated Attack Detection System
Production-ready detection with ML models
"""

from typing import List, Dict, Optional
from dataclasses import dataclass
from enum import Enum
import numpy as np
from sklearn.ensemble import IsolationForest
import logging

logger = logging.getLogger(__name__)

class ThreatLevel(Enum):
    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"
    CRITICAL = "critical"

@dataclass
class DetectionResult:
    threat_level: ThreatLevel
    confidence: float
    indicators: List[str]
    recommendation: str

class AIAttackDetector:
    """AI-generated attack detection system."""
    
    def __init__(self):
        # Initialize ML model for anomaly detection
        self.model = IsolationForest(contamination=0.1, random_state=42)
        self.is_trained = False
    
    def train_model(self, training_data: np.ndarray):
        """Train detection model on normal behavior."""
        try:
            self.model.fit(training_data)
            self.is_trained = True
            logger.info("Detection model trained successfully")
        except Exception as e:
            logger.error(f"Model training failed: {e}")
            raise
    
    def detect_ai_attack(self, behavior_features: np.ndarray) -> DetectionResult:
        """Detect AI-generated attack based on behavior."""
        if not self.is_trained:
            raise ValueError("Model not trained")
        
        # Predict anomaly
        prediction = self.model.predict([behavior_features])
        score = self.model.score_samples([behavior_features])[0]
        
        # Determine threat level
        if prediction[0] == -1:  # Anomaly detected
            if score < -0.5:
                threat_level = ThreatLevel.CRITICAL
            elif score < -0.3:
                threat_level = ThreatLevel.HIGH
            else:
                threat_level = ThreatLevel.MEDIUM
        else:
            threat_level = ThreatLevel.LOW
        
        # Extract indicators
        indicators = self.extract_indicators(behavior_features)
        
        return DetectionResult(
            threat_level=threat_level,
            confidence=abs(score),
            indicators=indicators,
            recommendation=self.get_recommendation(threat_level)
        )
    
    def extract_indicators(self, features: np.ndarray) -> List[str]:
        """Extract threat indicators from features."""
        indicators = []
        # Feature interpretation logic
        if features[0] > 0.8:  # Example: high adaptation rate
            indicators.append("Rapid behavior adaptation detected")
        if features[1] > 0.7:  # Example: polymorphic patterns
            indicators.append("Polymorphic signature patterns")
        return indicators
    
    def get_recommendation(self, threat_level: ThreatLevel) -> str:
        """Get response recommendation."""
        recommendations = {
            ThreatLevel.CRITICAL: "Immediate containment and investigation",
            ThreatLevel.HIGH: "Priority investigation required",
            ThreatLevel.MEDIUM: "Monitor and investigate",
            ThreatLevel.LOW: "Continue monitoring"
        }
        return recommendations.get(threat_level, "Monitor")

# Usage
detector = AIAttackDetector()

# Train on normal behavior (example)
normal_behavior = np.random.rand(100, 5)  # Example training data
detector.train_model(normal_behavior)

# Detect attack
suspicious_behavior = np.array([0.9, 0.8, 0.7, 0.6, 0.5])
result = detector.detect_ai_attack(suspicious_behavior)
print(f"Threat Level: {result.threat_level.value}")
print(f"Confidence: {result.confidence:.2f}")
print(f"Indicators: {result.indicators}")

#!/usr/bin/env python3
"""
Advanced Behavioral Analysis for AI Attack Detection
Production-ready behavioral analysis with ML integration
"""

from typing import List, Dict, Optional, Tuple
from dataclasses import dataclass, field, asdict
from enum import Enum
from datetime import datetime, timedelta
import numpy as np
from sklearn.ensemble import IsolationForest, RandomForestClassifier
from sklearn.preprocessing import StandardScaler
import logging
import json

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class ThreatLevel(Enum):
    """Threat level classification."""
    LOW = "low"
    MEDIUM = "medium"
    HIGH = "high"
    CRITICAL = "critical"

class AttackType(Enum):
    """Attack type classification."""
    POLYMORPHIC_MALWARE = "polymorphic_malware"
    AI_PHISHING = "ai_phishing"
    AUTOMATED_ATTACK = "automated_attack"
    ADAPTIVE_EVASION = "adaptive_evasion"
    UNKNOWN = "unknown"

@dataclass
class BehaviorFeature:
    """Behavioral feature extracted from activity."""
    feature_name: str
    value: float
    timestamp: datetime
    source: str

@dataclass
class DetectionResult:
    """Comprehensive detection result."""
    threat_level: ThreatLevel
    attack_type: AttackType
    confidence: float
    indicators: List[str]
    behavior_features: List[BehaviorFeature]
    recommendation: str
    detected_at: datetime = field(default_factory=datetime.now)
    
    def to_dict(self) -> Dict:
        """Convert to dictionary."""
        return {
            **asdict(self),
            'threat_level': self.threat_level.value,
            'attack_type': self.attack_type.value,
            'detected_at': self.detected_at.isoformat(),
            'behavior_features': [
                {
                    **asdict(bf),
                    'timestamp': bf.timestamp.isoformat()
                }
                for bf in self.behavior_features
            ]
        }

class BehaviorAnalyzer:
    """Analyzes behavior patterns for AI attack indicators."""
    
    def __init__(self):
        self.feature_history: List[BehaviorFeature] = []
        self.baseline_features: Dict[str, float] = {}
        self.is_baseline_established = False
    
    def establish_baseline(self, normal_behavior: List[Dict], window_days: int = 30):
        """Establish normal behavior baseline.
        
        Args:
            normal_behavior: List of normal behavior records
            window_days: Time window for baseline
        """
        try:
            # Extract features from normal behavior
            features = self._extract_features(normal_behavior)
            
            # Calculate baseline statistics
            for feature_name in features.keys():
                values = features[feature_name]
                self.baseline_features[feature_name] = {
                    'mean': np.mean(values),
                    'std': np.std(values),
                    'min': np.min(values),
                    'max': np.max(values)
                }
            
            self.is_baseline_established = True
            logger.info("Behavior baseline established")
            
        except Exception as e:
            logger.error(f"Failed to establish baseline: {e}", exc_info=True)
            raise
    
    def _extract_features(self, behavior_data: List[Dict]) -> Dict[str, List[float]]:
        """Extract behavioral features from data."""
        features = {
            'request_frequency': [],
            'request_pattern_variance': [],
            'response_time_variance': [],
            'error_rate': [],
            'unique_endpoints': [],
            'payload_size_variance': []
        }
        
        for record in behavior_data:
            features['request_frequency'].append(record.get('request_count', 0))
            features['error_rate'].append(record.get('error_rate', 0.0))
            features['unique_endpoints'].append(record.get('unique_endpoints', 0))
        
        return features
    
    def analyze_behavior(self, current_behavior: List[Dict]) -> Dict:
        """Analyze current behavior against baseline.
        
        Args:
            current_behavior: Current behavior records
            
        Returns:
            Analysis result dictionary
        """
        if not self.is_baseline_established:
            raise ValueError("Baseline not established")
        
        current_features = self._extract_features(current_behavior)
        anomalies = []
        anomaly_score = 0.0
        
        for feature_name, values in current_features.items():
            if feature_name not in self.baseline_features:
                continue
            
            baseline = self.baseline_features[feature_name]
            current_value = np.mean(values) if values else 0.0
            
            # Check for significant deviation
            z_score = abs((current_value - baseline['mean']) / (baseline['std'] + 1e-6))
            
            if z_score > 3.0:  # 3 standard deviations
                anomalies.append({
                    'feature': feature_name,
                    'current_value': current_value,
                    'baseline_mean': baseline['mean'],
                    'z_score': z_score,
                    'severity': 'high' if z_score > 5.0 else 'medium'
                })
                anomaly_score += z_score
        
        return {
            'anomalies': anomalies,
            'anomaly_score': anomaly_score,
            'is_anomalous': len(anomalies) > 0,
            'anomaly_count': len(anomalies)
        }

class AIAttackDetector:
    """Production-ready AI-generated attack detection system."""
    
    def __init__(self):
        self.model = IsolationForest(contamination=0.1, random_state=42)
        self.classifier = RandomForestClassifier(n_estimators=100, random_state=42)
        self.scaler = StandardScaler()
        self.behavior_analyzer = BehaviorAnalyzer()
        self.is_trained = False
        self.detections: List[DetectionResult] = []
    
    def train_model(self, normal_behavior: np.ndarray, attack_samples: Optional[np.ndarray] = None):
        """Train detection model.
        
        Args:
            normal_behavior: Normal behavior feature vectors
            attack_samples: Optional known attack samples
        """
        try:
            if attack_samples is not None and len(attack_samples) > 0:
                # Supervised learning
                X = np.vstack([normal_behavior, attack_samples])
                y = np.array([0] * len(normal_behavior) + [1] * len(attack_samples))
                
                X_scaled = self.scaler.fit_transform(X)
                self.classifier.fit(X_scaled, y)
                self.is_trained = True
            else:
                # Unsupervised learning
                X_scaled = self.scaler.fit_transform(normal_behavior)
                self.model.fit(X_scaled)
                self.is_trained = True
            
            # Establish behavior baseline
            normal_data = [{'request_count': int(x[0]), 'error_rate': float(x[1])} for x in normal_behavior]
            self.behavior_analyzer.establish_baseline(normal_data)
            
            logger.info("Detection model trained successfully")
            
        except Exception as e:
            logger.error(f"Model training failed: {e}", exc_info=True)
            raise
    
    def detect_ai_attack(self, behavior_features: np.ndarray, behavior_data: List[Dict]) -> DetectionResult:
        """Detect AI-generated attack.
        
        Args:
            behavior_features: Feature vector
            behavior_data: Raw behavior data
            
        Returns:
            DetectionResult object
        """
        if not self.is_trained:
            raise ValueError("Model not trained")
        
        # ML-based detection
        features_scaled = self.scaler.transform([behavior_features])
        
        if hasattr(self, 'classifier') and self.classifier is not None:
            prediction = self.classifier.predict(features_scaled)[0]
            probabilities = self.classifier.predict_proba(features_scaled)[0]
            confidence = probabilities[1] if len(probabilities) > 1 else 0.0
            is_anomaly = prediction == 1
        else:
            prediction = self.model.predict(features_scaled)[0]
            score = self.model.score_samples(features_scaled)[0]
            confidence = abs(score)
            is_anomaly = prediction == -1
        
        # Behavioral analysis
        behavior_analysis = self.behavior_analyzer.analyze_behavior(behavior_data)
        
        # Combine ML and behavioral analysis
        if is_anomaly or behavior_analysis['is_anomalous']:
            combined_confidence = (confidence + behavior_analysis['anomaly_score'] / 10.0) / 2.0
            combined_confidence = min(combined_confidence, 1.0)
            
            # Determine threat level
            if combined_confidence >= 0.9:
                threat_level = ThreatLevel.CRITICAL
            elif combined_confidence >= 0.7:
                threat_level = ThreatLevel.HIGH
            elif combined_confidence >= 0.5:
                threat_level = ThreatLevel.MEDIUM
            else:
                threat_level = ThreatLevel.LOW
            
            # Determine attack type
            attack_type = self._classify_attack_type(behavior_features, behavior_analysis)
            
            # Extract indicators
            indicators = self._extract_indicators(behavior_features, behavior_analysis)
            
            # Generate recommendation
            recommendation = self._generate_recommendation(threat_level, attack_type)
            
            result = DetectionResult(
                threat_level=threat_level,
                attack_type=attack_type,
                confidence=combined_confidence,
                indicators=indicators,
                behavior_features=[
                    BehaviorFeature(
                        feature_name=f"feature_{i}",
                        value=float(behavior_features[i]),
                        timestamp=datetime.now(),
                        source="ml_model"
                    )
                    for i in range(len(behavior_features))
                ],
                recommendation=recommendation
            )
            
            self.detections.append(result)
            logger.warning(f"AI attack detected: {attack_type.value}, confidence={combined_confidence:.2f}")
            return result
        else:
            return DetectionResult(
                threat_level=ThreatLevel.LOW,
                attack_type=AttackType.UNKNOWN,
                confidence=1.0 - confidence,
                indicators=[],
                behavior_features=[],
                recommendation="No action needed"
            )
    
    def _classify_attack_type(self, features: np.ndarray, behavior_analysis: Dict) -> AttackType:
        """Classify attack type based on features."""
        # Simplified classification logic
        if behavior_analysis.get('anomaly_count', 0) > 5:
            return AttackType.POLYMORPHIC_MALWARE
        elif features[0] > 100:  # High request frequency
            return AttackType.AUTOMATED_ATTACK
        else:
            return AttackType.ADAPTIVE_EVASION
    
    def _extract_indicators(self, features: np.ndarray, behavior_analysis: Dict) -> List[str]:
        """Extract threat indicators."""
        indicators = []
        
        if behavior_analysis.get('is_anomalous'):
            indicators.append("Behavioral anomalies detected")
        
        if features[0] > 100:
            indicators.append("Unusually high request frequency")
        
        if behavior_analysis.get('anomaly_count', 0) > 3:
            indicators.append("Multiple behavioral anomalies")
        
        return indicators
    
    def _generate_recommendation(self, threat_level: ThreatLevel, attack_type: AttackType) -> str:
        """Generate response recommendation."""
        if threat_level == ThreatLevel.CRITICAL:
            return "Immediate containment and investigation required"
        elif threat_level == ThreatLevel.HIGH:
            return "Priority investigation and response"
        elif threat_level == ThreatLevel.MEDIUM:
            return "Monitor and investigate"
        else:
            return "Review and verify"
    
    def get_statistics(self) -> Dict:
        """Get detection statistics."""
        return {
            'total_detections': len(self.detections),
            'by_threat_level': {
                level.value: len([d for d in self.detections if d.threat_level == level])
                for level in ThreatLevel
            },
            'by_attack_type': {
                atype.value: len([d for d in self.detections if d.attack_type == atype])
                for atype in AttackType
            },
            'avg_confidence': np.mean([d.confidence for d in self.detections]) if self.detections else 0.0
        }
    
    def cleanup(self):
        """Clean up resources."""
        logger.info("Cleaning up AI attack detector resources")
        # In production, close connections, save state, etc.

# Example usage
if __name__ == "__main__":
    detector = AIAttackDetector()
    
    # Train on normal behavior
    normal_behavior = np.random.rand(100, 6)  # 100 samples, 6 features
    detector.train_model(normal_behavior)
    
    # Detect attack
    suspicious_behavior = np.array([[150.0, 0.8, 50.0, 0.3, 200.0, 0.9]])  # Anomalous features
    behavior_data = [{'request_count': 150, 'error_rate': 0.8}]
    
    result = detector.detect_ai_attack(suspicious_behavior[0], behavior_data)
    print(f"Attack detected: {result.attack_type.value}")
    print(f"Threat level: {result.threat_level.value}")
    print(f"Confidence: {result.confidence:.2f}")
    
    # Get statistics
    stats = detector.get_statistics()
    print(f"\nStatistics: {json.dumps(stats, indent=2)}")

#!/usr/bin/env python3
"""
Unit tests for AI Attack Detector
"""

import pytest
import numpy as np
from ai_attack_detector import (
    AIAttackDetector, ThreatLevel, AttackType,
    BehaviorAnalyzer
)

class TestAIAttackDetector:
    """Tests for AIAttackDetector."""
    
    @pytest.fixture
    def detector(self):
        return AIAttackDetector()
    
    @pytest.fixture
    def normal_behavior(self):
        return np.random.rand(50, 6)
    
    def test_train_model(self, detector, normal_behavior):
        """Test model training."""
        detector.train_model(normal_behavior)
        assert detector.is_trained is True
    
    def test_detect_attack(self, detector, normal_behavior):
        """Test attack detection."""
        detector.train_model(normal_behavior)
        
        suspicious_features = np.array([150.0, 0.8, 50.0, 0.3, 200.0, 0.9])
        behavior_data = [{'request_count': 150, 'error_rate': 0.8}]
        
        result = detector.detect_ai_attack(suspicious_features, behavior_data)
        assert result.threat_level in ThreatLevel
        assert result.attack_type in AttackType
        assert 0.0 <= result.confidence <= 1.0

class TestBehaviorAnalyzer:
    """Tests for BehaviorAnalyzer."""
    
    @pytest.fixture
    def analyzer(self):
        return BehaviorAnalyzer()
    
    def test_establish_baseline(self, analyzer):
        """Test baseline establishment."""
        normal_data = [
            {'request_count': 10, 'error_rate': 0.1} for _ in range(100)
        ]
        analyzer.establish_baseline(normal_data)
        assert analyzer.is_baseline_established is True

if __name__ == "__main__":
    pytest.main([__file__, "-v"])

#!/usr/bin/env python3
"""
AI Attack Detector Cleanup
Production-ready cleanup and resource management
"""

import logging
from datetime import datetime, timedelta

logger = logging.getLogger(__name__)

class AIAttackDetectorCleanup:
    """Handles cleanup operations for AI attack detector."""
    
    def __init__(self, detector):
        """Initialize cleanup handler."""
        self.detector = detector
    
    def cleanup_old_detections(self, days: int = 90):
        """Remove detections older than specified days."""
        cutoff_date = datetime.now() - timedelta(days=days)
        initial_count = len(self.detector.detections)
        
        self.detector.detections = [
            d for d in self.detector.detections
            if d.detected_at >= cutoff_date
        ]
        
        removed = initial_count - len(self.detector.detections)
        logger.info(f"Cleaned up {removed} old detections")
        return removed
    
    def cleanup(self):
        """Perform complete cleanup."""
        logger.info("Starting AI attack detector cleanup")
        
        # Clean up old detections
        self.cleanup_old_detections()
        
        # Clean up detector resources
        self.detector.cleanup()
        
        logger.info("AI attack detector cleanup complete")