advanced_ai.py

"""
Simplified Neural-Symbolic AI for Hugging Face Space
Based on src/learning/neurosymbolic_ai.py
"""

import numpy as np
import json
from datetime import datetime
from typing import Dict, List, Any, Optional
import logging

class SimplifiedNeuroSymbolicAI:
    """Simplified neural-symbolic AI for cybersecurity analysis in HF Space"""
    
    def __init__(self):
        self.logger = logging.getLogger(__name__)
        
        # Cybersecurity knowledge rules (simplified)
        self.security_rules = {
            "malware_indicators": [
                "suspicious_process_execution",
                "network_communication_anomaly", 
                "file_modification_pattern",
                "registry_manipulation"
            ],
            "network_threats": [
                "port_scanning",
                "brute_force_attack",
                "ddos_pattern",
                "lateral_movement"
            ],
            "data_exfiltration": [
                "large_data_transfer",
                "encrypted_communication",
                "unusual_access_pattern",
                "external_connection"
            ]
        }
        
        # Threat severity mapping
        self.threat_severity = {
            "critical": {"score": 0.9, "action": "immediate_response"},
            "high": {"score": 0.7, "action": "urgent_investigation"},
            "medium": {"score": 0.5, "action": "monitor_closely"},
            "low": {"score": 0.3, "action": "routine_check"}
        }
    
    def analyze_threat_neural_symbolic(self, threat_data: str, 
                                     context: Optional[Dict] = None) -> Dict[str, Any]:
        """Perform neural-symbolic threat analysis"""
        
        analysis_id = f"ns_analysis_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
        
        # Neural processing (simplified)
        neural_features = self._extract_neural_features(threat_data)
        
        # Symbolic reasoning
        symbolic_analysis = self._symbolic_reasoning(threat_data, neural_features)
        
        # Integration
        integrated_result = self._integrate_analysis(neural_features, symbolic_analysis)
        
        return {
            "analysis_id": analysis_id,
            "timestamp": datetime.now().isoformat(),
            "threat_data": threat_data,
            "neural_analysis": {
                "feature_extraction": neural_features,
                "confidence": neural_features.get("confidence", 0.8)
            },
            "symbolic_analysis": symbolic_analysis,
            "integrated_result": integrated_result,
            "recommendations": self._generate_recommendations(integrated_result)
        }
    
    def _extract_neural_features(self, threat_data: str) -> Dict[str, Any]:
        """Extract neural features from threat data"""
        
        # Simulate neural network feature extraction
        features = {
            "anomaly_score": min(0.9, len(threat_data) / 100.0 + 0.3),
            "semantic_features": [],
            "behavioral_patterns": [],
            "confidence": 0.8
        }
        
        # Pattern recognition
        threat_lower = threat_data.lower()
        
        if any(term in threat_lower for term in ["malware", "virus", "trojan", "backdoor"]):
            features["semantic_features"].append("malware_related")
            features["anomaly_score"] += 0.2
            
        if any(term in threat_lower for term in ["network", "scan", "port", "connection"]):
            features["semantic_features"].append("network_activity")
            features["anomaly_score"] += 0.1
            
        if any(term in threat_lower for term in ["data", "exfiltration", "transfer", "leak"]):
            features["semantic_features"].append("data_movement")
            features["anomaly_score"] += 0.3
        
        # Behavioral pattern analysis
        if "suspicious" in threat_lower:
            features["behavioral_patterns"].append("suspicious_behavior")
        if "anomal" in threat_lower:
            features["behavioral_patterns"].append("anomalous_activity")
        if "attack" in threat_lower:
            features["behavioral_patterns"].append("attack_pattern")
            
        features["anomaly_score"] = min(0.95, features["anomaly_score"])
        
        return features
    
    def _symbolic_reasoning(self, threat_data: str, neural_features: Dict) -> Dict[str, Any]:
        """Apply symbolic reasoning rules"""
        
        conclusions = []
        applied_rules = []
        confidence_scores = []
        
        threat_lower = threat_data.lower()
        
        # Rule 1: Malware detection
        if any(indicator in neural_features.get("semantic_features", []) for indicator in ["malware_related"]):
            conclusions.append({
                "rule": "malware_detection_rule",
                "conclusion": "Potential malware activity detected",
                "confidence": 0.85,
                "evidence": neural_features["semantic_features"]
            })
            applied_rules.append("malware_detection_rule")
            confidence_scores.append(0.85)
        
        # Rule 2: Network threat assessment
        if "network_activity" in neural_features.get("semantic_features", []):
            network_confidence = 0.7
            if any(term in threat_lower for term in ["scan", "brute", "ddos"]):
                network_confidence = 0.9
                
            conclusions.append({
                "rule": "network_threat_rule", 
                "conclusion": "Network-based threat activity identified",
                "confidence": network_confidence,
                "evidence": ["network_activity_patterns"]
            })
            applied_rules.append("network_threat_rule")
            confidence_scores.append(network_confidence)
        
        # Rule 3: Data exfiltration risk
        if "data_movement" in neural_features.get("semantic_features", []):
            conclusions.append({
                "rule": "data_exfiltration_rule",
                "conclusion": "Potential data exfiltration attempt detected",
                "confidence": 0.8,
                "evidence": ["unusual_data_transfer_patterns"]
            })
            applied_rules.append("data_exfiltration_rule")
            confidence_scores.append(0.8)
        
        # Rule 4: Behavioral anomaly
        if neural_features["anomaly_score"] > 0.7:
            conclusions.append({
                "rule": "behavioral_anomaly_rule",
                "conclusion": "High behavioral anomaly detected",
                "confidence": neural_features["anomaly_score"],
                "evidence": neural_features["behavioral_patterns"]
            })
            applied_rules.append("behavioral_anomaly_rule")
            confidence_scores.append(neural_features["anomaly_score"])
        
        return {
            "conclusions": conclusions,
            "applied_rules": applied_rules,
            "overall_confidence": np.mean(confidence_scores) if confidence_scores else 0.5,
            "reasoning_steps": len(conclusions)
        }
    
    def _integrate_analysis(self, neural_features: Dict, symbolic_analysis: Dict) -> Dict[str, Any]:
        """Integrate neural and symbolic analysis results"""
        
        # Calculate overall threat level
        neural_score = neural_features["anomaly_score"]
        symbolic_score = symbolic_analysis["overall_confidence"]
        
        integrated_score = (neural_score + symbolic_score) / 2
        
        # Determine threat level
        if integrated_score >= 0.8:
            threat_level = "CRITICAL"
            severity = "critical"
        elif integrated_score >= 0.6:
            threat_level = "HIGH" 
            severity = "high"
        elif integrated_score >= 0.4:
            threat_level = "MEDIUM"
            severity = "medium"
        else:
            threat_level = "LOW"
            severity = "low"
        
        return {
            "threat_level": threat_level,
            "severity": severity,
            "integrated_score": round(integrated_score, 3),
            "neural_contribution": round(neural_score, 3),
            "symbolic_contribution": round(symbolic_score, 3),
            "confidence": min(0.95, integrated_score),
            "explanation": self._generate_explanation(neural_features, symbolic_analysis, threat_level)
        }
    
    def _generate_explanation(self, neural_features: Dict, symbolic_analysis: Dict, threat_level: str) -> str:
        """Generate human-readable explanation"""
        
        explanation_parts = [
            f"🔍 Analysis indicates {threat_level} threat level based on:",
            "",
            "🧠 Neural Analysis:",
            f"  • Anomaly Score: {neural_features['anomaly_score']:.2f}",
            f"  • Detected Features: {', '.join(neural_features.get('semantic_features', ['none']))}", 
            f"  • Behavioral Patterns: {', '.join(neural_features.get('behavioral_patterns', ['none']))}",
            "",
            "🔗 Symbolic Reasoning:",
            f"  • Rules Applied: {len(symbolic_analysis['applied_rules'])}",
            f"  • Conclusions: {len(symbolic_analysis['conclusions'])}",
            f"  • Confidence: {symbolic_analysis['overall_confidence']:.2f}",
        ]
        
        if symbolic_analysis["conclusions"]:
            explanation_parts.append("  • Key Findings:")
            for conclusion in symbolic_analysis["conclusions"][:3]:
                explanation_parts.append(f"    - {conclusion['conclusion']} (confidence: {conclusion['confidence']:.2f})")
        
        return "\n".join(explanation_parts)
    
    def _generate_recommendations(self, integrated_result: Dict) -> List[str]:
        """Generate actionable security recommendations"""
        
        severity = integrated_result["severity"]
        threat_level = integrated_result["threat_level"]
        
        recommendations = []
        
        # Base recommendations by severity
        severity_info = self.threat_severity.get(severity, self.threat_severity["medium"])
        
        if severity == "critical":
            recommendations.extend([
                "🚨 IMMEDIATE ACTION REQUIRED",
                "• Initiate incident response procedures",
                "• Isolate affected systems immediately",
                "• Contact security team and management",
                "• Begin forensic data collection"
            ])
        elif severity == "high":
            recommendations.extend([
                "⚠️  URGENT INVESTIGATION NEEDED", 
                "• Deploy additional monitoring on affected systems",
                "• Implement network segmentation if possible",
                "• Escalate to security analysts",
                "• Review related security logs"
            ])
        elif severity == "medium":
            recommendations.extend([
                "🔍 CLOSE MONITORING RECOMMENDED",
                "• Increase logging and monitoring",
                "• Schedule security review within 24 hours", 
                "• Implement additional access controls",
                "• Update threat intelligence feeds"
            ])
        else:
            recommendations.extend([
                "✅ ROUTINE SECURITY MEASURES",
                "• Continue normal monitoring", 
                "• Document findings for future reference",
                "• Regular security updates recommended"
            ])
        
        # Add specific recommendations based on analysis
        recommendations.append("\n🛡️  SPECIFIC SECURITY MEASURES:")
        recommendations.extend([
            "• Update antivirus and security signatures",
            "• Review network access controls", 
            "• Validate backup and recovery procedures",
            "• Consider threat hunting activities"
        ])
        
        return recommendations

# Initialize global instance for the Space
neuro_symbolic_ai = SimplifiedNeuroSymbolicAI()