Autonomous, Self-Evolving Malware: The Future Cyber Threat

#!/usr/bin/env python3
"""
Autonomous Malware Detection System
Production-ready detection for self-evolving malware
"""

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

class EvolutionIndicator(Enum):
    CODE_MUTATION = "code_mutation"
    BEHAVIOR_CHANGE = "behavior_change"
    ADAPTIVE_EVASION = "adaptive_evasion"
    SELF_MODIFICATION = "self_modification"

@dataclass
class MalwareDetection:
    detection_id: str
    indicator: EvolutionIndicator
    confidence: float
    behavioral_features: np.ndarray

class AutonomousMalwareDetector:
    """Autonomous malware detection system."""
    
    def __init__(self):
        self.model = IsolationForest(contamination=0.1)
        self.is_trained = False
        self.detections: List[MalwareDetection] = []
    
    def train_on_normal_behavior(self, normal_samples: np.ndarray):
        """Train model on normal behavior."""
        self.model.fit(normal_samples)
        self.is_trained = True
    
    def detect_evolution(self, behavioral_features: np.ndarray) -> MalwareDetection:
        """Detect autonomous malware evolution."""
        if not self.is_trained:
            raise ValueError("Model not trained")
        
        prediction = self.model.predict([behavioral_features])
        score = self.model.score_samples([behavioral_features])[0]
        
        # Determine evolution indicator
        indicator = EvolutionIndicator.BEHAVIOR_CHANGE
        if score < -0.5:
            indicator = EvolutionIndicator.ADAPTIVE_EVASION
        elif score < -0.3:
            indicator = EvolutionIndicator.CODE_MUTATION
        
        detection = MalwareDetection(
            detection_id=f"DET-{len(self.detections)+1}",
            indicator=indicator,
            confidence=abs(score),
            behavioral_features=behavioral_features
        )
        
        self.detections.append(detection)
        return detection

# Usage
detector = AutonomousMalwareDetector()
normal_behavior = np.random.rand(100, 5)
detector.train_on_normal_behavior(normal_behavior)

suspicious_behavior = np.array([0.9, 0.8, 0.7, 0.6, 0.5])
detection = detector.detect_evolution(suspicious_behavior)
print(f"Evolution detected: {detection.indicator.value}")

#!/usr/bin/env python3
"""
Advanced Autonomous Malware Detection System
Production-ready behavioral analysis with evolution tracking
"""

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

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

class EvolutionIndicator(Enum):
    CODE_MUTATION = "code_mutation"
    BEHAVIOR_CHANGE = "behavior_change"
    ADAPTIVE_EVASION = "adaptive_evasion"
    SELF_MODIFICATION = "self_modification"
    LEARNING_BEHAVIOR = "learning_behavior"

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

@dataclass
class BehavioralFeature:
    """Behavioral feature extracted from activity."""
    feature_name: str
    value: float
    timestamp: datetime
    source: str
    
    def to_dict(self) -> Dict:
        """Convert to dictionary."""
        return {
            **asdict(self),
            'timestamp': self.timestamp.isoformat()
        }

@dataclass
class MalwareDetection:
    """Autonomous malware detection result."""
    detection_id: str
    indicator: EvolutionIndicator
    confidence: float
    threat_level: ThreatLevel
    behavioral_features: List[BehavioralFeature]
    evolution_score: float
    timestamp: datetime = field(default_factory=datetime.now)
    
    def to_dict(self) -> Dict:
        """Convert to dictionary."""
        return {
            **asdict(self),
            'indicator': self.indicator.value,
            'threat_level': self.threat_level.value,
            'timestamp': self.timestamp.isoformat(),
            'behavioral_features': [bf.to_dict() for bf in self.behavioral_features]
        }

class AdvancedAutonomousMalwareDetector:
    """Production-ready autonomous malware detection system."""
    
    def __init__(self):
        self.isolation_model = IsolationForest(contamination=0.1, random_state=42)
        self.classifier = RandomForestClassifier(n_estimators=100, random_state=42)
        self.scaler = StandardScaler()
        self.is_trained = False
        self.detections: List[MalwareDetection] = []
        self.behavior_history: List[Dict] = []
        self.evolution_tracking: Dict[str, List[float]] = {}
    
    def train_on_normal_behavior(self, normal_samples: np.ndarray):
        """Train model on normal behavior.
        
        Args:
            normal_samples: Normal behavior feature vectors
        """
        try:
            X_scaled = self.scaler.fit_transform(normal_samples)
            self.isolation_model.fit(X_scaled)
            self.is_trained = True
            logger.info("Model trained on normal behavior")
        except Exception as e:
            logger.error(f"Training failed: {e}", exc_info=True)
            raise
    
    def detect_evolution(self, behavioral_features: np.ndarray, process_id: str) -> MalwareDetection:
        """Detect autonomous malware evolution.
        
        Args:
            behavioral_features: Current behavioral features
            process_id: Process identifier
            
        Returns:
            MalwareDetection result
        """
        if not self.is_trained:
            raise ValueError("Model not trained")
        
        # Track evolution over time
        if process_id not in self.evolution_tracking:
            self.evolution_tracking[process_id] = []
        
        # Scale features
        features_scaled = self.scaler.transform([behavioral_features])[0]
        
        # Isolation Forest detection
        prediction = self.isolation_model.predict([features_scaled])[0]
        score = self.isolation_model.score_samples([features_scaled])[0]
        
        # Track evolution score
        evolution_score = abs(score)
        self.evolution_tracking[process_id].append(evolution_score)
        
        # Check for evolution pattern
        evolution_pattern = self._analyze_evolution_pattern(process_id)
        
        # Determine evolution indicator
        if evolution_pattern.get('rapid_change', False):
            indicator = EvolutionIndicator.ADAPTIVE_EVASION
        elif evolution_pattern.get('continuous_change', False):
            indicator = EvolutionIndicator.LEARNING_BEHAVIOR
        elif prediction == -1:
            indicator = EvolutionIndicator.BEHAVIOR_CHANGE
        else:
            indicator = EvolutionIndicator.CODE_MUTATION
        
        # Determine threat level
        if evolution_score >= 0.8 or evolution_pattern.get('rapid_change', False):
            threat_level = ThreatLevel.CRITICAL
        elif evolution_score >= 0.6:
            threat_level = ThreatLevel.HIGH
        elif evolution_score >= 0.4:
            threat_level = ThreatLevel.MEDIUM
        else:
            threat_level = ThreatLevel.LOW
        
        # Create behavioral features
        behavioral_feature_list = [
            BehavioralFeature(
                feature_name=f"feature_{i}",
                value=float(behavioral_features[i]),
                timestamp=datetime.now(),
                source="behavioral_analysis"
            )
            for i in range(len(behavioral_features))
        ]
        
        detection = MalwareDetection(
            detection_id=f"DET-{len(self.detections)+1}",
            indicator=indicator,
            confidence=evolution_score,
            threat_level=threat_level,
            behavioral_features=behavioral_feature_list,
            evolution_score=evolution_score
        )
        
        self.detections.append(detection)
        self.behavior_history.append({
            'process_id': process_id,
            'features': behavioral_features.tolist(),
            'timestamp': datetime.now().isoformat()
        })
        
        logger.warning(f"Evolution detected: {indicator.value}, score={evolution_score:.2f}")
        return detection
    
    def _analyze_evolution_pattern(self, process_id: str) -> Dict:
        """Analyze evolution pattern for process.
        
        Args:
            process_id: Process identifier
            
        Returns:
            Evolution pattern analysis
        """
        scores = self.evolution_tracking.get(process_id, [])
        
        if len(scores) < 3:
            return {'rapid_change': False, 'continuous_change': False}
        
        # Check for rapid change
        recent_scores = scores[-3:]
        score_variance = np.var(recent_scores)
        if score_variance > 0.1:
            return {'rapid_change': True, 'continuous_change': False}
        
        # Check for continuous change
        if len(scores) >= 5:
            trend = np.polyfit(range(len(scores)), scores, 1)[0]
            if abs(trend) > 0.05:
                return {'rapid_change': False, 'continuous_change': True}
        
        return {'rapid_change': False, 'continuous_change': False}
    
    def get_evolution_statistics(self, process_id: Optional[str] = None) -> Dict:
        """Get evolution statistics.
        
        Args:
            process_id: Optional process ID to filter
            
        Returns:
            Statistics dictionary
        """
        if process_id:
            scores = self.evolution_tracking.get(process_id, [])
            return {
                'process_id': process_id,
                'evolution_events': len(scores),
                'avg_evolution_score': np.mean(scores) if scores else 0.0,
                'max_evolution_score': np.max(scores) if scores else 0.0,
                'trend': 'increasing' if len(scores) > 1 and scores[-1] > scores[0] else 'stable'
            }
        else:
            return {
                'total_processes': len(self.evolution_tracking),
                'total_detections': len(self.detections),
                'by_indicator': {
                    ind.value: len([d for d in self.detections if d.indicator == ind])
                    for ind in EvolutionIndicator
                },
                'by_threat_level': {
                    level.value: len([d for d in self.detections if d.threat_level == level])
                    for level in ThreatLevel
                }
            }
    
    def cleanup(self):
        """Clean up resources."""
        logger.info("Cleaning up autonomous malware detector resources")

# Example usage
if __name__ == "__main__":
    detector = AdvancedAutonomousMalwareDetector()
    
    # Train on normal behavior
    normal_behavior = np.random.rand(100, 5)
    detector.train_on_normal_behavior(normal_behavior)
    
    # Detect evolution
    suspicious_behavior = np.array([0.9, 0.8, 0.7, 0.6, 0.5])
    detection = detector.detect_evolution(suspicious_behavior, "PROC-001")
    
    print(f"Evolution detected: {detection.indicator.value}")
    print(f"Confidence: {detection.confidence:.2f}")
    print(f"Threat level: {detection.threat_level.value}")
    
    stats = detector.get_evolution_statistics()
    print(f"Statistics: {json.dumps(stats, indent=2)}")

#!/usr/bin/env python3
"""
Unit tests for Autonomous Malware Detector
"""

import pytest
import numpy as np
from autonomous_malware_detector import (
    AdvancedAutonomousMalwareDetector, EvolutionIndicator, ThreatLevel
)

class TestAutonomousMalwareDetector:
    """Tests for AdvancedAutonomousMalwareDetector."""
    
    @pytest.fixture
    def detector(self):
        return AdvancedAutonomousMalwareDetector()
    
    @pytest.fixture
    def normal_behavior(self):
        return np.random.rand(50, 5)
    
    def test_train_model(self, detector, normal_behavior):
        """Test model training."""
        detector.train_on_normal_behavior(normal_behavior)
        assert detector.is_trained is True
    
    def test_detect_evolution(self, detector, normal_behavior):
        """Test evolution detection."""
        detector.train_on_normal_behavior(normal_behavior)
        suspicious = np.array([0.9, 0.8, 0.7, 0.6, 0.5])
        detection = detector.detect_evolution(suspicious, "TEST-001")
        assert detection.indicator in EvolutionIndicator
        assert detection.threat_level in ThreatLevel

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

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

import logging
from datetime import datetime, timedelta

logger = logging.getLogger(__name__)

class AutonomousMalwareDetectorCleanup:
    """Handles cleanup operations."""
    
    def __init__(self, detector):
        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.timestamp >= 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 autonomous malware detector cleanup")
        self.cleanup_old_detections()
        self.detector.cleanup()
        logger.info("Autonomous malware detector cleanup complete")

Real-World Case Study

Challenge: Organization facing persistent malware:

• Traditional detection ineffective
• Malware adapting to defenses
• Persistent infections
• Difficult remediation

Solution: Implemented advanced detection:

• Behavioral analysis systems
• ML-based detection
• Anomaly detection
• Threat hunting
• Incident response

Results:

• Detection improvement: Behavioral analysis effective
• Threat elimination: Advanced detection identifies threats
• Reduced persistence: Rapid detection enables faster response
• Security posture improved: Better visibility and defense

FAQ

Q: How does autonomous malware evolve?

A: Uses AI/ML to analyze defenses, adapt behaviors, modify code, and evolve based on feedback. Continuously learns and improves evasion techniques.

Q: Can traditional AV detect autonomous malware?

A: Limited effectiveness. Signature-based detection struggles with polymorphic, adaptive malware. Requires behavioral analysis and ML-based detection.

Q: How do I defend against autonomous malware?

A: Use behavioral analysis, ML-based detection, anomaly detection, network segmentation, zero trust, and continuous monitoring. Defense-in-depth essential.

Conclusion

Autonomous malware represents a significant threat. Implement behavioral analysis, ML-based detection, and defense-in-depth to detect and defend against self-evolving threats.

Action Steps

1. Understand autonomous malware
2. Implement behavioral analysis
3. Deploy ML-based detection
4. Use anomaly detection
5. Network segmentation
6. Zero trust architecture
7. Continuous monitoring

Related Topics

• Detecting AI-Generated Attacks
• Threat Hunting 2026

Educational Use Only: This content is for educational purposes. Implement advanced detection to defend against autonomous malware.