# Example: Incident classification system
from enum import Enum
from dataclasses import dataclass
from typing import Dict, List, Optional
import json
from datetime import datetime

class IncidentSeverity(Enum):
    CRITICAL = "critical"
    HIGH = "high"
    MEDIUM = "medium"
    LOW = "low"
    INFORMATIONAL = "informational"

class IncidentCategory(Enum):
    APPLICATION_SECURITY = "application_security"
    INFRASTRUCTURE_SECURITY = "infrastructure_security"
    DATA_BREACH = "data_breach"
    COMPLIANCE_VIOLATION = "compliance_violation"
    SERVICE_AVAILABILITY = "service_availability"
    DEPLOYMENT_ISSUE = "deployment_issue"

class IncidentType(Enum):
    MALWARE_DETECTION = "malware_detection"
    UNAUTHORIZED_ACCESS = "unauthorized_access"
    DATA_EXPOSURE = "data_exposure"
    DDOS_ATTACK = "ddos_attack"
    PRIVILEGE_ESCALATION = "privilege_escalation"
    CONFIGURATION_MISUSE = "configuration_misuse"
    SUPPLY_CHAIN_COMPROMISE = "supply_chain_compromise"
    INSIDER_THREAT = "insider_threat"

@dataclass
class Incident:
    id: str
    title: str
    description: str
    severity: IncidentSeverity
    category: IncidentCategory
    incident_type: IncidentType
    detected_at: datetime
    source: str  # Where incident was detected
    affected_services: List[str]
    affected_users: int
    estimated_impact: str
    status: str = "investigating"
    assigned_to: Optional[str] = None
    escalated_to: Optional[str] = None

class IncidentClassifier:
    def __init__(self):
        self.classification_rules = self.load_classification_rules()
    
    def load_classification_rules(self) -> Dict:
        """
        Load incident classification rules
        """
        return {
            "application_security": {
                "high_severity_indicators": [
                    "sql_injection",
                    "remote_code_execution",
                    "authentication_bypass"
                ],
                "medium_severity_indicators": [
                    "cross_site_scripting",
                    "insecure_deserialization"
                ]
            },
            "data_breach": {
                "critical_indicators": [
                    "personal_identifiable_information_exposed",
                    "credit_card_numbers_exposed",
                    "protected_health_information_exposed"
                ]
            },
            "infrastructure_security": {
                "critical_indicators": [
                    "root_level_compromise",
                    "network_perimeter_breach",
                    "cloud_admin_compromise"
                ]
            }
        }
    
    def classify_incident(self, raw_alert: Dict) -> Incident:
        """
        Classify an incoming security alert as an incident
        """
        # Determine severity based on indicators
        severity = self.determine_severity(raw_alert)
        
        # Determine category based on context
        category = self.determine_category(raw_alert)
        
        # Determine specific incident type
        incident_type = self.determine_incident_type(raw_alert)
        
        # Extract affected services
        affected_services = self.extract_affected_services(raw_alert)
        
        # Create incident object
        incident = Incident(
            id=f"INC-{datetime.utcnow().strftime('%Y%m%d')}-{hash(str(raw_alert)) % 10000:04d}",
            title=raw_alert.get('title', 'Security Alert'),
            description=raw_alert.get('description', ''),
            severity=severity,
            category=category,
            incident_type=incident_type,
            detected_at=datetime.utcnow(),
            source=raw_alert.get('source', 'unknown'),
            affected_services=affected_services,
            affected_users=raw_alert.get('affected_users', 0),
            estimated_impact=self.estimate_impact(severity, category, affected_services),
            status="investigating"
        )
        
        return incident
    
    def determine_severity(self, alert: Dict) -> IncidentSeverity:
        """
        Determine incident severity based on alert characteristics
        """
        # Check for critical indicators
        if any(indicator in alert.get('indicators', []) for indicator in [
            'root_compromise', 'data_exfiltration_detected', 'ransomware_detected'
        ]):
            return IncidentSeverity.CRITICAL
        
        # Check for high severity indicators
        if any(indicator in alert.get('indicators', []) for indicator in [
            'unauthorized_access', 'privilege_escalation', 'sql_injection'
        ]):
            return IncidentSeverity.HIGH
        
        # Check for medium severity indicators
        if any(indicator in alert.get('indicators', []) for indicator in [
            'suspicious_activity', 'failed_login_attempts', 'unusual_api_calls'
        ]):
            return IncidentSeverity.MEDIUM
        
        return IncidentSeverity.LOW
    
    def determine_category(self, alert: Dict) -> IncidentCategory:
        """
        Determine incident category based on alert characteristics
        """
        # Map alert source to category
        source_map = {
            'waf': IncidentCategory.APPLICATION_SECURITY,
            'ids': IncidentCategory.INFRASTRUCTURE_SECURITY,
            'detection_service': IncidentCategory.DATA_BREACH,
            'compliance_monitor': IncidentCategory.COMPLIANCE_VIOLATION,
            'availability_monitor': IncidentCategory.SERVICE_AVAILABILITY,
            'deployment_monitor': IncidentCategory.DEPLOYMENT_ISSUE
        }
        
        source = alert.get('source', 'unknown').lower()
        for key, category in source_map.items():
            if key in source:
                return category
        
        # Default to application security if no specific mapping found
        return IncidentCategory.APPLICATION_SECURITY
    
    def determine_incident_type(self, alert: Dict) -> IncidentType:
        """
        Determine specific incident type
        """
        # Check for specific indicators
        indicators = alert.get('indicators', [])
        
        type_map = {
            'malware': IncidentType.MALWARE_DETECTION,
            'unauthorized_access': IncidentType.UNAUTHORIZED_ACCESS,
            'data_exposure': IncidentType.DATA_EXPOSURE,
            'ddos': IncidentType.DDOS_ATTACK,
            'privilege_escalation': IncidentType.PRIVILEGE_ESCALATION,
            'misconfiguration': IncidentType.CONFIGURATION_MISUSE,
            'supply_chain': IncidentType.SUPPLY_CHAIN_COMPROMISE,
            'insider': IncidentType.INSIDER_THREAT
        }
        
        for indicator in indicators:
            for key, incident_type in type_map.items():
                if key in indicator.lower():
                    return incident_type
        
        # Default to unauthorized access if no specific type identified
        return IncidentType.UNAUTHORIZED_ACCESS
    
    def extract_affected_services(self, alert: Dict) -> List[str]:
        """
        Extract affected services from alert
        """
        affected = []
        
        # Extract from alert metadata
        if 'affected_services' in alert:
            affected.extend(alert['affected_services'])
        
        # Extract from resource identifiers
        if 'resources' in alert:
            for resource in alert['resources']:
                if 'service' in resource:
                    affected.append(resource['service'])
        
        # Extract from application context
        if 'application' in alert:
            affected.append(alert['application'])
        
        return list(set(affected))  # Remove duplicates
    
    def estimate_impact(self, severity: IncidentSeverity, category: IncidentCategory, affected_services: List[str]) -> str:
        """
        Estimate incident impact
        """
        impact_scores = {
            IncidentSeverity.CRITICAL: 10,
            IncidentSeverity.HIGH: 7,
            IncidentSeverity.MEDIUM: 4,
            IncidentSeverity.LOW: 2,
            IncidentSeverity.INFORMATIONAL: 1
        }
        
        # Base impact on severity
        impact_score = impact_scores[severity]
        
        # Adjust for category
        if category in [IncidentCategory.DATA_BREACH, IncidentCategory.COMPLIANCE_VIOLATION]:
            impact_score += 2  # Higher impact for data/compliance incidents
        
        # Adjust for number of affected services
        impact_score += len(affected_services)
        
        # Convert to qualitative impact
        if impact_score >= 15:
            return "Severe - Significant business impact"
        elif impact_score >= 10:
            return "High - Major business impact"
        elif impact_score >= 6:
            return "Medium - Moderate business impact"
        else:
            return "Low - Minimal business impact"

# Example usage
classifier = IncidentClassifier()

# Example security alert
sample_alert = {
    "title": "SQL Injection Attempt Detected",
    "description": "Multiple SQL injection attempts detected on user authentication endpoint",
    "source": "web_application_firewall",
    "indicators": ["sql_injection", "unauthorized_access_attempt"],
    "resources": [
        {"service": "auth-service", "instance": "auth-service-123"},
        {"service": "user-service", "instance": "user-service-456"}
    ],
    "application": "customer_portal",
    "affected_users": 0,
    "timestamp": datetime.utcnow().isoformat()
}

# Classify the incident
incident = classifier.classify_incident(sample_alert)
print(f"Classified incident: {incident.id}")
print(f"Severity: {incident.severity.value}")
print(f"Category: {incident.category.value}")
print(f"Impact: {incident.estimated_impact}")

# Example: Automated incident escalation system
class IncidentEscalationSystem:
    def __init__(self):
        self.escalation_rules = self.define_escalation_rules()
        self.response_teams = self.load_response_teams()
    
    def define_escalation_rules(self) -> Dict:
        """
        Define escalation rules based on incident characteristics
        """
        return {
            "critical_severity": {
                "immediate_escalation": True,
                "notification_targets": ["security_ops", "cto", "legal"],
                "maximum_response_time": 15  # minutes
            },
            "data_breach": {
                "immediate_escalation": True,
                "notification_targets": ["privacy_officer", "legal", "executive"],
                "regulatory_notification_required": True
            },
            "service_outage": {
                "business_impact": True,
                "notification_targets": ["operations", "product_managers", "customers"]
            },
            "supply_chain": {
                "wide_impact": True,
                "notification_targets": ["security", "engineering", "vendor_management"]
            }
        }
    
    def load_response_teams(self) -> Dict:
        """
        Load response team information
        """
        return {
            "security_ops": {
                "contact": "[email protected]",
                "members": ["Alice", "Bob", "Charlie"],
                "on_call_rotation": "24x7"
            },
            "engineering": {
                "contact": "[email protected]",
                "members": ["David", "Eve", "Frank"],
                "on_call_rotation": "business_hours"
            },
            "legal": {
                "contact": "[email protected]",
                "members": ["Grace", "Heidi"],
                "on_call_rotation": "business_hours"
            }
        }
    
    def determine_escalation_path(self, incident: Incident) -> Dict:
        """
        Determine escalation path for an incident
        """
        escalation_path = {
            "immediate_escalation": False,
            "teams_to_notify": [],
            "regulatory_notification_required": False,
            "executive_notification_required": False,
            "escalation_reasons": []
        }
        
        # Check for critical severity
        if incident.severity == IncidentSeverity.CRITICAL:
            escalation_path["immediate_escalation"] = True
            escalation_path["teams_to_notify"].extend(["security_ops", "executive"])
            escalation_path["escalation_reasons"].append("Critical severity incident")
        
        # Check for data breach category
        if incident.category == IncidentCategory.DATA_BREACH:
            escalation_path["regulatory_notification_required"] = True
            escalation_path["teams_to_notify"].extend(["privacy_officer", "legal"])
            escalation_path["executive_notification_required"] = True
            escalation_path["escalation_reasons"].append("Data breach detected")
        
        # Check for compliance violation
        if incident.category == IncidentCategory.COMPLIANCE_VIOLATION:
            escalation_path["teams_to_notify"].append("compliance_team")
            escalation_path["regulatory_notification_required"] = True
            escalation_path["escalation_reasons"].append("Compliance violation detected")
        
        # Check for service availability issues
        if incident.category == IncidentCategory.SERVICE_AVAILABILITY:
            escalation_path["teams_to_notify"].append("operations")
            escalation_path["executive_notification_required"] = len(incident.affected_services) > 3
            escalation_path["escalation_reasons"].append("Service availability impacted")
        
        # Remove duplicates
        escalation_path["teams_to_notify"] = list(set(escalation_path["teams_to_notify"]))
        
        return escalation_path
    
    def execute_escalation(self, incident: Incident):
        """
        Execute escalation for an incident
        """
        escalation_path = self.determine_escalation_path(incident)
        
        # Send notifications to required teams
        for team_name in escalation_path["teams_to_notify"]:
            if team_name in self.response_teams:
                team = self.response_teams[team_name]
                self.send_notification(
                    team["contact"],
                    f"Incident {incident.id} requires immediate attention",
                    incident
                )
        
        # Handle regulatory notifications if required
        if escalation_path["regulatory_notification_required"]:
            self.handle_regulatory_notification(incident)
        
        # Handle executive notifications if required
        if escalation_path["executive_notification_required"]:
            self.handle_executive_notification(incident)
        
        print(f"Escalation executed for incident {incident.id}")
        return escalation_path

# Example usage
escalation_system = IncidentEscalationSystem()

# Example incident that should trigger escalation
critical_incident = Incident(
    id="TEST-INC-001",
    title="Critical Security Breach",
    description="Unauthorized access to customer database detected",
    severity=IncidentSeverity.CRITICAL,
    category=IncidentCategory.DATA_BREACH,
    incident_type=IncidentType.UNAUTHORIZED_ACCESS,
    detected_at=datetime.utcnow(),
    source="database_monitor",
    affected_services=["customer_db", "api_gateway"],
    affected_users=10000,
    estimated_impact="Severe - Significant business impact"
)

escalation_result = escalation_system.execute_escalation(critical_incident)
print(f"Escalation result: {escalation_result}")

# Example: Threat intelligence integration system
import requests
import feedparser
from datetime import datetime, timedelta
import re
from typing import List, Dict, Any

class ThreatIntelligenceFeed:
    def __init__(self, feed_urls: List[str]):
        self.feed_urls = feed_urls
        self.last_update = None
        self.threat_indicators = []
        self.ioc_cache = {}  # Indicator of Compromise cache
    
    def fetch_threat_feeds(self) -> List[Dict[str, Any]]:
        """
        Fetch threat intelligence from multiple feeds
        """
        all_threats = []
        
        for feed_url in self.feed_urls:
            try:
                if feed_url.endswith('.rss') or feed_url.endswith('.xml'):
                    # RSS/Atom feed
                    feed = feedparser.parse(feed_url)
                    threats = self.parse_rss_feed(feed)
                else:
                    # JSON feed
                    response = requests.get(feed_url)
                    threats = response.json()
                
                all_threats.extend(threats)
                
            except Exception as e:
                print(f"Error fetching threat feed {feed_url}: {str(e)}")
        
        return all_threats
    
    def parse_rss_feed(self, feed) -> List[Dict[str, Any]]:
        """
        Parse RSS threat feed
        """
        threats = []
        
        for entry in feed.entries:
            threat = {
                'title': entry.title,
                'description': getattr(entry, 'summary', ''),
                'published': getattr(entry, 'published', ''),
                'link': getattr(entry, 'link', ''),
                'severity': self.assess_threat_severity(entry.title, entry.summary if hasattr(entry, 'summary') else ''),
                'indicators': self.extract_indicators(entry.summary if hasattr(entry, 'summary') else ''),
                'tags': self.extract_tags(entry.tags if hasattr(entry, 'tags', []) else [])
            }
            threats.append(threat)
        
        return threats
    
    def assess_threat_severity(self, title: str, description: str) -> str:
        """
        Assess threat severity based on title and description
        """
        high_severity_keywords = [
            'critical', 'urgent', 'exploit', 'zero-day', 'ransomware', 
            'data-breach', 'compromise', 'attack'
        ]
        
        medium_severity_keywords = [
            'vulnerability', 'patch', 'security-update', 'advisory'
        ]
        
        title_lower = title.lower()
        desc_lower = description.lower()
        
        for keyword in high_severity_keywords:
            if keyword in title_lower or keyword in desc_lower:
                return 'high'
        
        for keyword in medium_severity_keywords:
            if keyword in title_lower or keyword in desc_lower:
                return 'medium'
        
        return 'low'
    
    def extract_indicators(self, text: str) -> List[str]:
        """
        Extract IOCs from threat text
        """
        indicators = []
        
        # Extract IP addresses
        ip_pattern = r'\b(?:[0-9]{1,3}\.){3}[0-9]{1,3}\b'
        ips = re.findall(ip_pattern, text)
        indicators.extend(ips)
        
        # Extract domains
        domain_pattern = r'\b[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}\b'
        domains = re.findall(domain_pattern, text)
        # Filter out common domains
        indicators.extend([d for d in domains if d not in ['example.com', 'test.com']])
        
        # Extract URLs
        url_pattern = r'https?://[^\s<>"{}|\\^`\[\]]+'
        urls = re.findall(url_pattern, text)
        indicators.extend(urls)
        
        # Extract file hashes (MD5, SHA-1, SHA-256)
        hash_patterns = [
            r'\b[a-fA-F0-9]{32}\b',  # MD5
            r'\b[a-fA-F0-9]{40}\b',  # SHA-1
            r'\b[a-fA-F0-9]{64}\b'   # SHA-256
        ]
        
        for pattern in hash_patterns:
            hashes = re.findall(pattern, text)
            indicators.extend(hashes)
        
        return list(set(indicators))  # Remove duplicates
    
    def extract_tags(self, tags: List[Dict]) -> List[str]:
        """
        Extract tags from feed entry
        """
        return [tag.term for tag in tags if hasattr(tag, 'term')]
    
    def update_threat_indicators(self):
        """
        Update threat indicators from feeds
        """
        new_threats = self.fetch_threat_feeds()
        
        # Update internal threat indicators
        for threat in new_threats:
            for indicator in threat['indicators']:
                self.ioc_cache[indicator] = {
                    'threat': threat,
                    'first_seen': datetime.utcnow(),
                    'last_seen': datetime.utcnow()
                }
        
        self.last_update = datetime.utcnow()
        self.threat_indicators = new_threats

class DevSecOpsThreatMonitor:
    def __init__(self, threat_feeds: List[str]):
        self.threat_feed = ThreatIntelligenceFeed(threat_feeds)
        self.active_monitors = []
        self.alert_subscribers = []
    
    def start_monitoring(self):
        """
        Start continuous threat monitoring
        """
        # Update threat indicators
        self.threat_feed.update_threat_indicators()
        
        # Set up monitoring for various data sources
        self.setup_log_monitoring()
        self.setup_network_monitoring()
        self.setup_application_monitoring()
        
        print("Threat monitoring started")
    
    def setup_log_monitoring(self):
        """
        Set up log monitoring for threat indicators
        """
        # This would typically connect to your logging system
        log_monitor = {
            'name': 'log_monitor',
            'data_source': 'centralized_logs',
            'ioc_matcher': self.match_iocs_in_logs,
            'frequency': 'real_time'
        }
        self.active_monitors.append(log_monitor)
    
    def setup_network_monitoring(self):
        """
        Set up network monitoring for threat indicators
        """
        network_monitor = {
            'name': 'network_monitor',
            'data_source': 'network_traffic',
            'ioc_matcher': self.match_iocs_in_network,
            'frequency': 'real_time'
        }
        self.active_monitors.append(network_monitor)
    
    def setup_application_monitoring(self):
        """
        Set up application monitoring for threat indicators
        """
        app_monitor = {
            'name': 'application_monitor',
            'data_source': 'application_logs',
            'ioc_matcher': self.match_iocs_in_app_logs,
            'frequency': 'real_time'
        }
        self.active_monitors.append(app_monitor)
    
    def match_iocs_in_logs(self, log_entry: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
        Match IOCs in log entries
        """
        matches = []
        
        # Convert log entry to searchable text
        log_text = self.log_entry_to_text(log_entry)
        
        # Check against cached IOCs
        for ioc, ioc_info in self.threat_feed.ioc_cache.items():
            if ioc.lower() in log_text.lower():
                match = {
                    'ioc': ioc,
                    'matched_in': 'logs',
                    'log_entry': log_entry,
                    'threat_info': ioc_info['threat'],
                    'match_confidence': 0.9  # High confidence for direct match
                }
                matches.append(match)
        
        return matches
    
    def match_iocs_in_network(self, network_data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
        Match IOCs in network data
        """
        matches = []
        
        # Check network data for IOCs
        for ioc, ioc_info in self.threat_feed.ioc_cache.items():
            # Check if IOC is an IP address
            if re.match(r'\b(?:[0-9]{1,3}\.){3}[0-9]{1,3}\b', ioc):
                if network_data.get('destination_ip') == ioc or network_data.get('source_ip') == ioc:
                    match = {
                        'ioc': ioc,
                        'matched_in': 'network',
                        'network_data': network_data,
                        'threat_info': ioc_info['threat'],
                        'match_confidence': 0.8
                    }
                    matches.append(match)
            
            # Check for domain matches
            elif '.' in ioc and len(ioc) > 3:  # Likely a domain
                if network_data.get('destination_domain') == ioc:
                    match = {
                        'ioc': ioc,
                        'matched_in': 'network',
                        'network_data': network_data,
                        'threat_info': ioc_info['threat'],
                        'match_confidence': 0.7
                    }
                    matches.append(match)
        
        return matches
    
    def match_iocs_in_app_logs(self, app_log: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
        Match IOCs in application logs
        """
        matches = []
        
        # Convert app log to searchable text
        log_text = self.app_log_to_text(app_log)
        
        # Check against cached IOCs
        for ioc, ioc_info in self.threat_feed.ioc_cache.items():
            if ioc.lower() in log_text.lower():
                match = {
                    'ioc': ioc,
                    'matched_in': 'application_logs',
                    'app_log': app_log,
                    'threat_info': ioc_info['threat'],
                    'match_confidence': 0.85
                }
                matches.append(match)
        
        return matches
    
    def log_entry_to_text(self, log_entry: Dict[str, Any]) -> str:
        """
        Convert log entry to searchable text
        """
        text_parts = []
        
        for key, value in log_entry.items():
            if isinstance(value, str):
                text_parts.append(value)
            elif isinstance(value, (int, float)):
                text_parts.append(str(value))
        
        return ' '.join(text_parts)
    
    def app_log_to_text(self, app_log: Dict[str, Any]) -> str:
        """
        Convert app log to searchable text
        """
        text_parts = []
        
        if 'message' in app_log:
            text_parts.append(app_log['message'])
        
        if 'params' in app_log:
            for param_value in app_log['params'].values():
                if isinstance(param_value, str):
                    text_parts.append(param_value)
        
        return ' '.join(text_parts)
    
    def monitor_for_threats(self) -> List[Dict[str, Any]]:
        """
        Monitor all data sources for threats
        """
        all_matches = []
        
        for monitor in self.active_monitors:
            # In a real implementation, this would fetch recent data from the data source
            # For this example, we'll simulate with mock data
            recent_data = self.get_recent_data(monitor['data_source'])
            
            for data_item in recent_data:
                matches = monitor['ioc_matcher'](data_item)
                all_matches.extend(matches)
        
        # Filter and prioritize matches
        prioritized_matches = self.prioritize_matches(all_matches)
        
        return prioritized_matches
    
    def get_recent_data(self, data_source: str) -> List[Dict[str, Any]]:
        """
        Get recent data from data source (mock implementation)
        """
        # This would connect to actual data sources in a real implementation
        mock_data = {
            'centralized_logs': [
                {'timestamp': '2023-12-01T10:30:00Z', 'message': 'Connection from malicious IP 192.168.1.100', 'level': 'WARNING'},
                {'timestamp': '2023-12-01T10:31:00Z', 'message': 'Suspicious API call detected', 'level': 'INFO'}
            ],
            'network_traffic': [
                {'source_ip': '192.168.1.100', 'destination_ip': '10.0.0.1', 'port': 443, 'protocol': 'HTTPS'},
                {'source_ip': '203.0.113.42', 'destination_ip': '10.0.0.5', 'port': 80, 'protocol': 'HTTP'}
            ],
            'application_logs': [
                {'timestamp': '2023-12-01T10:30:00Z', 'message': 'SQL injection attempt', 'user_id': '12345', 'ip_address': '192.168.1.100'},
                {'timestamp': '2023-12-01T10:31:00Z', 'message': 'Successful login', 'user_id': '67890', 'ip_address': '192.168.1.50'}
            ]
        }
        
        return mock_data.get(data_source, [])
    
    def prioritize_matches(self, matches: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        """
        Prioritize threat matches based on severity and confidence
        """
        # Sort by threat severity and match confidence
        def match_priority(match):
            threat_severity = match['threat_info']['severity']
            confidence = match['match_confidence']
            
            severity_weights = {'high': 3, 'medium': 2, 'low': 1}
            severity_weight = severity_weights.get(threat_severity, 1)
            
            return (severity_weight * 100) + (confidence * 10)
        
        return sorted(matches, key=match_priority, reverse=True)

# Example usage
threat_feeds = [
    'https://feeds.feedburner.com/ThreatPost',
    'https://www.cisa.gov/cybersecurity-advisories/all.xml'
]

threat_monitor = DevSecOpsThreatMonitor(threat_feeds)
threat_monitor.start_monitoring()

# Monitor for threats
threat_matches = threat_monitor.monitor_for_threats()
print(f"Found {len(threat_matches)} potential threats")
for match in threat_matches[:3]:  # Show first 3 matches
    print(f"Match: {match['ioc']} - Confidence: {match['match_confidence']}")
    print(f"Threat: {match['threat_info']['title']}")
    print("---")

# Example: Threat response orchestration system
import asyncio
import aiohttp
from datetime import datetime
from typing import Dict, List, Any

class ThreatResponseOrchestrator:
    def __init__(self):
        self.response_playbooks = self.load_response_playbooks()
        self.security_tools = self.initialize_security_tools()
        self.response_history = []
    
    def load_response_playbooks(self) -> Dict[str, Any]:
        """
        Load threat response playbooks
        """
        return {
            'malware_detection': {
                'name': 'Malware Detection Response',
                'steps': [
                    {'action': 'contain_system', 'priority': 1},
                    {'action': 'collect_forensics', 'priority': 2},
                    {'action': 'analyze_malware', 'priority': 3},
                    {'action': 'remediate', 'priority': 4},
                    {'action': 'restore_system', 'priority': 5}
                ],
                'required_tools': ['EDR', 'forensics_tool', 'malware_analyzer']
            },
            'ddos_attack': {
                'name': 'DDoS Attack Response',
                'steps': [
                    {'action': 'activate_mitigation', 'priority': 1},
                    {'action': 'monitor_traffic', 'priority': 2},
                    {'action': 'adjust_rate_limits', 'priority': 3},
                    {'action': 'coordinate_with_isp', 'priority': 4}
                ],
                'required_tools': ['firewall', 'traffic_analyzer', 'rate_limiter']
            },
            'data_exfiltration': {
                'name': 'Data Exfiltration Response',
                'steps': [
                    {'action': 'block_exfil_channels', 'priority': 1},
                    {'action': 'preserve_evidence', 'priority': 2},
                    {'action': 'investigate_source', 'priority': 3},
                    {'action': 'notify_stakeholders', 'priority': 4}
                ],
                'required_tools': ['IDS', 'DLP', 'forensics_tool']
            }
        }
    
    def initialize_security_tools(self) -> Dict[str, Any]:
        """
        Initialize connections to security tools
        """
        return {
            'EDR': {'connected': True, 'api_endpoint': 'https://edr.company.com/api'},
            'firewall': {'connected': True, 'api_endpoint': 'https://firewall.company.com/api'},
            'IDS': {'connected': True, 'api_endpoint': 'https://ids.company.com/api'},
            'DLP': {'connected': True, 'api_endpoint': 'https://dlp.company.com/api'},
            'forensics_tool': {'connected': True, 'api_endpoint': 'https://forensics.company.com/api'},
            'malware_analyzer': {'connected': True, 'api_endpoint': 'https://malware.company.com/api'},
            'traffic_analyzer': {'connected': True, 'api_endpoint': 'https://traffic.company.com/api'},
            'rate_limiter': {'connected': True, 'api_endpoint': 'https://rate-limit.company.com/api'}
        }
    
    async def execute_response_plan(self, threat_type: str, threat_details: Dict[str, Any]) -> Dict[str, Any]:
        """
        Execute automated response plan for a threat
        """
        if threat_type not in self.response_playbooks:
            return {'error': f'No playbook found for threat type: {threat_type}'}
        
        playbook = self.response_playbooks[threat_type]
        
        # Check if required tools are available
        for tool in playbook['required_tools']:
            if tool not in self.security_tools or not self.security_tools[tool]['connected']:
                return {'error': f'Required tool {tool} is not available'}
        
        # Execute response steps
        results = {
            'threat_type': threat_type,
            'threat_details': threat_details,
            'start_time': datetime.utcnow().isoformat(),
            'steps_executed': [],
            'errors': []
        }
        
        # Sort steps by priority
        sorted_steps = sorted(playbook['steps'], key=lambda x: x['priority'])
        
        for step in sorted_steps:
            try:
                step_result = await self.execute_step(step, threat_details)
                results['steps_executed'].append({
                    'step': step['action'],
                    'status': 'success',
                    'result': step_result
                })
            except Exception as e:
                results['steps_executed'].append({
                    'step': step['action'],
                    'status': 'error',
                    'error': str(e)
                })
                results['errors'].append(str(e))
        
        results['end_time'] = datetime.utcnow().isoformat()
        results['duration'] = self.calculate_duration(results['start_time'], results['end_time'])
        
        # Add to response history
        self.response_history.append(results)
        
        return results
    
    async def execute_step(self, step: Dict[str, Any], threat_details: Dict[str, Any]) -> Any:
        """
        Execute a single response step
        """
        step_name = step['action']
        
        if step_name == 'contain_system':
            return await self.contain_system(threat_details)
        elif step_name == 'collect_forensics':
            return await self.collect_forensics(threat_details)
        elif step_name == 'analyze_malware':
            return await self.analyze_malware(threat_details)
        elif step_name == 'remediate':
            return await self.remediate_system(threat_details)
        elif step_name == 'restore_system':
            return await self.restore_system(threat_details)
        elif step_name == 'activate_mitigation':
            return await self.activate_ddos_mitigation(threat_details)
        elif step_name == 'monitor_traffic':
            return await self.monitor_network_traffic(threat_details)
        elif step_name == 'adjust_rate_limits':
            return await self.adjust_rate_limits(threat_details)
        elif step_name == 'block_exfil_channels':
            return await self.block_exfiltration_channels(threat_details)
        elif step_name == 'preserve_evidence':
            return await self.preserve_evidence(threat_details)
        elif step_name == 'investigate_source':
            return await self.investigate_source(threat_details)
        elif step_name == 'notify_stakeholders':
            return await self.notify_stakeholders(threat_details)
        else:
            raise ValueError(f'Unknown step: {step_name}')
    
    async def contain_system(self, threat_details: Dict[str, Any]) -> str:
        """
        Contain compromised system
        """
        # In a real implementation, this would isolate the affected system
        system_id = threat_details.get('system_id', 'unknown')
        
        # Mock API call to EDR tool
        async with aiohttp.ClientSession() as session:
            async with session.post(
                f"{self.security_tools['EDR']['api_endpoint']}/isolate-system",
                json={'system_id': system_id}
            ) as response:
                result = await response.json()
        
        return f"System {system_id} isolated: {result}"
    
    async def collect_forensics(self, threat_details: Dict[str, Any]) -> str:
        """
        Collect forensics data from affected system
        """
        system_id = threat_details.get('system_id', 'unknown')
        
        # Mock forensics collection
        async with aiohttp.ClientSession() as session:
            async with session.post(
                f"{self.security_tools['forensics_tool']['api_endpoint']}/collect",
                json={'system_id': system_id, 'evidence_types': ['memory_dump', 'disk_image', 'network_logs']}
            ) as response:
                result = await response.json()
        
        return f"Forensics collected from {system_id}: {result}"
    
    async def analyze_malware(self, threat_details: Dict[str, Any]) -> str:
        """
        Analyze malware sample
        """
        if 'malware_sample' not in threat_details:
            return "No malware sample provided for analysis"
        
        sample_hash = threat_details['malware_sample']
        
        # Mock malware analysis
        async with aiohttp.ClientSession() as session:
            async with session.post(
                f"{self.security_tools['malware_analyzer']['api_endpoint']}/analyze",
                json={'sample_hash': sample_hash}
            ) as response:
                result = await response.json()
        
        return f"Malware analysis completed: {result}"
    
    async def remediate_system(self, threat_details: Dict[str, Any]) -> str:
        """
        Remediate compromised system
        """
        system_id = threat_details.get('system_id', 'unknown')
        
        # Mock remediation
        async with aiohttp.ClientSession() as session:
            async with session.post(
                f"{self.security_tools['EDR']['api_endpoint']}/remediate",
                json={'system_id': system_id, 'actions': ['remove_malware', 'reset_credentials', 'apply_patches']}
            ) as response:
                result = await response.json()
        
        return f"System {system_id} remediated: {result}"
    
    async def restore_system(self, threat_details: Dict[str, Any]) -> str:
        """
        Restore system to operational state
        """
        system_id = threat_details.get('system_id', 'unknown')
        
        # Mock restoration
        async with aiohttp.ClientSession() as session:
            async with session.post(
                f"{self.security_tools['EDR']['api_endpoint']}/restore",
                json={'system_id': system_id}
            ) as response:
                result = await response.json()
        
        return f"System {system_id} restored: {result}"
    
    async def activate_ddos_mitigation(self, threat_details: Dict[str, Any]) -> str:
        """
        Activate DDoS mitigation measures
        """
        attack_details = threat_details.get('attack_details', {})
        
        # Mock DDoS mitigation
        async with aiohttp.ClientSession() as session:
            async with session.post(
                f"{self.security_tools['firewall']['api_endpoint']}/activate-mitigation",
                json={'attack_type': attack_details.get('type'), 'target': attack_details.get('target')}
            ) as response:
                result = await response.json()
        
        return f"DDoS mitigation activated: {result}"
    
    async def monitor_network_traffic(self, threat_details: Dict[str, Any]) -> str:
        """
        Monitor network traffic for attack patterns
        """
        # Mock network monitoring
        async with aiohttp.ClientSession() as session:
            async with session.post(
                f"{self.security_tools['traffic_analyzer']['api_endpoint']}/start-monitoring",
                json={'monitor_duration': '30m', 'focus_areas': ['bandwidth', 'connection_patterns', 'geographic_origin']}
            ) as response:
                result = await response.json()
        
        return f"Network monitoring started: {result}"
    
    async def adjust_rate_limits(self, threat_details: Dict[str, Any]) -> str:
        """
        Adjust rate limiting to mitigate attack
        """
        # Mock rate limit adjustment
        async with aiohttp.ClientSession() as session:
            async with session.post(
                f"{self.security_tools['rate_limiter']['api_endpoint']}/adjust-limits",
                json={'new_limits': {'requests_per_minute': 100, 'concurrent_connections': 50}}
            ) as response:
                result = await response.json()
        
        return f"Rate limits adjusted: {result}"
    
    async def block_exfiltration_channels(self, threat_details: Dict[str, Any]) -> str:
        """
        Block data exfiltration channels
        """
        exfil_channels = threat_details.get('exfil_channels', [])
        
        # Mock blocking of exfiltration channels
        async with aiohttp.ClientSession() as session:
            async with session.post(
                f"{self.security_tools['firewall']['api_endpoint']}/block-channels",
                json={'channels': exfil_channels}
            ) as response:
                result = await response.json()
        
        return f"Exfiltration channels blocked: {result}"
    
    async def preserve_evidence(self, threat_details: Dict[str, Any]) -> str:
        """
        Preserve evidence of data exfiltration
        """
        # Mock evidence preservation
        async with aiohttp.ClientSession() as session:
            async with session.post(
                f"{self.security_tools['forensics_tool']['api_endpoint']}/preserve-evidence",
                json={'evidence_location': threat_details.get('evidence_location', 'unknown')}
            ) as response:
                result = await response.json()
        
        return f"Evidence preserved: {result}"
    
    async def investigate_source(self, threat_details: Dict[str, Any]) -> str:
        """
        Investigate source of data exfiltration
        """
        # Mock source investigation
        async with aiohttp.ClientSession() as session:
            async with session.post(
                f"{self.security_tools['IDS']['api_endpoint']}/investigate-source",
                json={'target_system': threat_details.get('target_system', 'unknown')}
            ) as response:
                result = await response.json()
        
        return f"Source investigation completed: {result}"
    
    async def notify_stakeholders(self, threat_details: Dict[str, Any]) -> str:
        """
        Notify relevant stakeholders of data exfiltration
        """
        # Mock notification
        stakeholders = threat_details.get('stakeholders', ['security_team'])
        
        # In a real implementation, this would send notifications
        return f"Notified stakeholders: {stakeholders}"
    
    def calculate_duration(self, start_time: str, end_time: str) -> str:
        """
        Calculate duration between two timestamps
        """
        start = datetime.fromisoformat(start_time.replace('Z', '+00:00'))
        end = datetime.fromisoformat(end_time.replace('Z', '+00:00'))
        duration = end - start
        return str(duration)

# Example usage
orchestrator = ThreatResponseOrchestrator()

# Example threat scenario
malware_threat = {
    'threat_type': 'malware_detection',
    'system_id': 'web-server-001',
    'malware_sample': 'abc123def456ghi789',
    'severity': 'high',
    'detected_at': datetime.utcnow().isoformat()
}

# Execute response plan
async def run_example():
    result = await orchestrator.execute_response_plan('malware_detection', malware_threat)
    print(json.dumps(result, indent=2))

# Run the example
# asyncio.run(run_example())

# Example: Automated forensics data collection pipeline
import os
import subprocess
import tempfile
import gzip
import hashlib
from datetime import datetime
import json
import shutil
from typing import Dict, List, Any

class AutomatedForensicsPipeline:
    def __init__(self, storage_path: str = "/var/forensics"):
        self.storage_path = storage_path
        self.tools = self.discover_forensics_tools()
        self.case_id_counter = 1000
    
    def discover_forensics_tools(self) -> Dict[str, str]:
        """
        Discover available forensics tools on the system
        """
        tools = {}
        
        # Check for common forensics tools
        tool_paths = {
            'volatility': shutil.which('volatility'),
            'sleuthkit': shutil.which('fls'),
            'autopsy': shutil.which('autopsy'),
            'tcpdump': shutil.which('tcpdump'),
            'wireshark': shutil.which('tshark'),
            'plaso': shutil.which('log2timeline.py')
        }
        
        for tool, path in tool_paths.items():
            if path:
                tools[tool] = path
        
        return tools
    
    def create_forensics_case(self, incident_id: str, description: str) -> str:
        """
        Create a new forensics case directory
        """
        case_id = f"FRS-{datetime.utcnow().strftime('%Y%m%d')}-{self.case_id_counter:04d}"
        self.case_id_counter += 1
        
        case_path = os.path.join(self.storage_path, case_id)
        os.makedirs(case_path, exist_ok=True)
        
        # Create case metadata
        metadata = {
            'case_id': case_id,
            'incident_id': incident_id,
            'description': description,
            'created_at': datetime.utcnow().isoformat(),
            'evidence_directories': {
                'memory_dumps': os.path.join(case_path, 'memory_dumps'),
                'disk_images': os.path.join(case_path, 'disk_images'),
                'network_logs': os.path.join(case_path, 'network_logs'),
                'process_logs': os.path.join(case_path, 'process_logs'),
                'registry_dumps': os.path.join(case_path, 'registry_dumps'),
                'timeline_data': os.path.join(case_path, 'timeline_data')
            }
        }
        
        # Create evidence subdirectories
        for dir_path in metadata['evidence_directories'].values():
            os.makedirs(dir_path, exist_ok=True)
        
        # Save case metadata
        with open(os.path.join(case_path, 'case_metadata.json'), 'w') as f:
            json.dump(metadata, f, indent=2)
        
        return case_id
    
    def collect_memory_dump(self, case_id: str, system_identifier: str) -> str:
        """
        Collect memory dump from system
        """
        case_path = os.path.join(self.storage_path, case_id)
        
        if 'volatility' not in self.tools:
            raise Exception("Volatility tool not available for memory dumps")
        
        # Create memory dump file
        dump_filename = f"memory_dump_{system_identifier}_{datetime.utcnow().strftime('%Y%m%d_%H%M%S')}.raw"
        dump_path = os.path.join(case_path, 'memory_dumps', dump_filename)
        
        # In a real implementation, this would collect the actual memory dump
        # For this example, we'll create a mock dump file
        with open(dump_path, 'wb') as f:
            # Write mock memory dump data
            mock_data = b'\x00' * (100 * 1024 * 1024)  # 100MB of null bytes
            f.write(mock_data)
        
        # Calculate hash of memory dump
        hash_value = self.calculate_file_hash(dump_path)
        
        # Create analysis metadata
        analysis_metadata = {
            'filename': dump_filename,
            'size_bytes': os.path.getsize(dump_path),
            'sha256_hash': hash_value,
            'collected_at': datetime.utcnow().isoformat(),
            'system_identifier': system_identifier,
            'analysis_completed': False
        }
        
        # Save analysis metadata
        metadata_path = os.path.join(case_path, 'memory_dumps', f"{dump_filename}.metadata.json")
        with open(metadata_path, 'w') as f:
            json.dump(analysis_metadata, f, indent=2)
        
        return dump_path
    
    def collect_disk_image(self, case_id: str, volume_path: str, system_identifier: str) -> str:
        """
        Collect disk image from volume
        """
        case_path = os.path.join(self.storage_path, case_id)
        
        if 'sleuthkit' not in self.tools:
            raise Exception("SleuthKit not available for disk imaging")
        
        # Create disk image file
        image_filename = f"disk_image_{system_identifier}_{datetime.utcnow().strftime('%Y%m%d_%H%M%S')}.dd"
        image_path = os.path.join(case_path, 'disk_images', image_filename)
        
        # In a real implementation, this would use dd or similar to create disk image
        # For this example, we'll create a mock disk image
        with open(image_path, 'wb') as f:
            # Write mock disk image data
            mock_data = b'\x00' * (500 * 1024 * 1024)  # 500MB of null bytes
            f.write(mock_data)
        
        # Calculate hash of disk image
        hash_value = self.calculate_file_hash(image_path)
        
        # Create analysis metadata
        analysis_metadata = {
            'filename': image_filename,
            'size_bytes': os.path.getsize(image_path),
            'sha256_hash': hash_value,
            'collected_at': datetime.utcnow().isoformat(),
            'system_identifier': system_identifier,
            'source_volume': volume_path,
            'analysis_completed': False
        }
        
        # Save analysis metadata
        metadata_path = os.path.join(case_path, 'disk_images', f"{image_filename}.metadata.json")
        with open(metadata_path, 'w') as f:
            json.dump(analysis_metadata, f, indent=2)
        
        return image_path
    
    def collect_network_logs(self, case_id: str, time_range: tuple, system_identifier: str) -> str:
        """
        Collect network logs for specified time range
        """
        case_path = os.path.join(self.storage_path, case_id)
        
        if 'tcpdump' not in self.tools:
            raise Exception("Tcpdump not available for network capture")
        
        # Create network capture file
        capture_filename = f"network_capture_{system_identifier}_{datetime.utcnow().strftime('%Y%m%d_%H%M%S')}.pcap"
        capture_path = os.path.join(case_path, 'network_logs', capture_filename)
        
        # In a real implementation, this would run tcpdump or similar
        # For this example, we'll create a mock capture file
        with open(capture_path, 'wb') as f:
            # Write mock network capture data
            mock_data = b'\x00' * (50 * 1024 * 1024)  # 50MB of null bytes
            f.write(mock_data)
        
        # Calculate hash of capture
        hash_value = self.calculate_file_hash(capture_path)
        
        # Create analysis metadata
        analysis_metadata = {
            'filename': capture_filename,
            'size_bytes': os.path.getsize(capture_path),
            'sha256_hash': hash_value,
            'collected_at': datetime.utcnow().isoformat(),
            'system_identifier': system_identifier,
            'time_range': time_range,
            'analysis_completed': False
        }
        
        # Save analysis metadata
        metadata_path = os.path.join(case_path, 'network_logs', f"{capture_filename}.metadata.json")
        with open(metadata_path, 'w') as f:
            json.dump(analysis_metadata, f, indent=2)
        
        return capture_path
    
    def collect_process_logs(self, case_id: str, system_identifier: str) -> str:
        """
        Collect process logs and information
        """
        case_path = os.path.join(self.storage_path, case_id)
        
        # Create process logs file
        logs_filename = f"process_logs_{system_identifier}_{datetime.utcnow().strftime('%Y%m%d_%H%M%S')}.json"
        logs_path = os.path.join(case_path, 'process_logs', logs_filename)
        
        # In a real implementation, this would collect actual process information
        # For this example, we'll create mock process data
        mock_processes = [
            {
                'pid': 1234,
                'name': 'suspicious_process.exe',
                'cmdline': 'powershell -encodedcommand ...',
                'user': 'SYSTEM',
                'created_at': '2023-12-01T10:30:00Z',
                'network_connections': [
                    {'local_port': 49152, 'remote_ip': '192.168.1.100', 'remote_port': 80}
                ]
            },
            {
                'pid': 5678,
                'name': 'legitimate_service.exe',
                'cmdline': 'service start',
                'user': 'SYSTEM',
                'created_at': '2023-12-01T09:00:00Z',
                'network_connections': []
            }
        ]
        
        with open(logs_path, 'w') as f:
            json.dump(mock_processes, f, indent=2)
        
        # Calculate hash of logs
        hash_value = self.calculate_file_hash(logs_path)
        
        # Create analysis metadata
        analysis_metadata = {
            'filename': logs_filename,
            'size_bytes': os.path.getsize(logs_path),
            'sha256_hash': hash_value,
            'collected_at': datetime.utcnow().isoformat(),
            'system_identifier': system_identifier,
            'process_count': len(mock_processes),
            'analysis_completed': False
        }
        
        # Save analysis metadata
        metadata_path = os.path.join(case_path, 'process_logs', f"{logs_filename}.metadata.json")
        with open(metadata_path, 'w') as f:
            json.dump(analysis_metadata, f, indent=2)
        
        return logs_path
    
    def calculate_file_hash(self, filepath: str) -> str:
        """
        Calculate SHA-256 hash of file
        """
        sha256_hash = hashlib.sha256()
        with open(filepath, "rb") as f:
            # Read file in chunks to handle large files
            for byte_block in iter(lambda: f.read(4096), b""):
                sha256_hash.update(byte_block)
        return sha256_hash.hexdigest()
    
    def run_volatility_analysis(self, case_id: str, memory_dump_path: str) -> Dict[str, Any]:
        """
        Run volatility analysis on memory dump
        """
        if 'volatility' not in self.tools:
            raise Exception("Volatility tool not available")
        
        case_path = os.path.join(self.storage_path, case_id)
        analysis_dir = os.path.join(case_path, 'analysis_results')
        os.makedirs(analysis_dir, exist_ok=True)
        
        # Run common volatility plugins
        plugins_to_run = [
            'pslist',      # Process list
            'netscan',     # Network connections
            'dlllist',     # Loaded DLLs
            'handles',     # Open handles
            'cmdline',     # Process command lines
            'malfind',     # Malware detection
            'yarascan'     # Yara rule scanning
        ]
        
        analysis_results = {
            'memory_dump': memory_dump_path,
            'analysis_started': datetime.utcnow().isoformat(),
            'plugins_run': [],
            'findings': []
        }
        
        for plugin in plugins_to_run:
            try:
                # In a real implementation, this would run the actual volatility command
                # For this example, we'll simulate the output
                plugin_output = self.simulate_volatility_plugin(plugin)
                
                plugin_result = {
                    'plugin': plugin,
                    'status': 'completed',
                    'output_file': os.path.join(analysis_dir, f"{plugin}_output.txt"),
                    'findings_count': len(plugin_output) if isinstance(plugin_output, list) else 0
                }
                
                # Save plugin output
                with open(plugin_result['output_file'], 'w') as f:
                    if isinstance(plugin_output, list):
                        for item in plugin_output:
                            f.write(f"{item}\n")
                    else:
                        f.write(str(plugin_output))
                
                analysis_results['plugins_run'].append(plugin_result)
                
                # Extract interesting findings
                if plugin == 'malfind':
                    analysis_results['findings'].extend([
                        f"Suspicious process detected: {finding}" 
                        for finding in plugin_output if 'suspicious' in str(finding).lower()
                    ])
                
            except Exception as e:
                plugin_result = {
                    'plugin': plugin,
                    'status': 'error',
                    'error': str(e)
                }
                analysis_results['plugins_run'].append(plugin_result)
        
        analysis_results['analysis_completed'] = datetime.utcnow().isoformat()
        
        # Save overall analysis results
        results_path = os.path.join(analysis_dir, 'volatility_analysis.json')
        with open(results_path, 'w') as f:
            json.dump(analysis_results, f, indent=2)
        
        return analysis_results
    
    def simulate_volatility_plugin(self, plugin: str) -> Any:
        """
        Simulate volatility plugin output
        """
        if plugin == 'pslist':
            return [
                "PID: 1234, Name: suspicious_process.exe, PPID: 567, Time: 2023-12-01 10:30:00",
                "PID: 5678, Name: legitimate_service.exe, PPID: 4, Time: 2023-12-01 09:00:00"
            ]
        elif plugin == 'netscan':
            return [
                "Proto: TCP, Local: 0.0.0.0:445, Remote: - , PID: 4",
                "Proto: TCP, Local: 192.168.1.50:49152, Remote: 192.168.1.100:80, PID: 1234"
            ]
        elif plugin == 'malfind':
            return [
                "Process: 1234, suspicious_process.exe, injected code detected",
                "Process: 5678, legitimate_service.exe, clean"
            ]
        elif plugin == 'yarascan':
            return [
                "Signature: SUSPICIOUS_POWERSHELL_CMD, Offset: 0x123456, Process: 1234"
            ]
        else:
            return ["Sample output for plugin: " + plugin]
    
    def generate_timeline(self, case_id: str) -> str:
        """
        Generate forensic timeline from collected evidence
        """
        case_path = os.path.join(self.storage_path, case_id)
        timeline_path = os.path.join(case_path, 'timeline_data', f"timeline_{datetime.utcnow().strftime('%Y%m%d_%H%M%S')}.csv")
        
        # In a real implementation, this would combine data from all evidence sources
        # For this example, we'll create a mock timeline
        timeline_entries = [
            "Timestamp,Event Type,Description,Source",
            "2023-12-01T09:00:00Z,System Boot,System started,Boot log",
            "2023-12-01T10:29:45Z,Network Connection,Outbound connection to 192.168.1.100,Net logs",
            "2023-12-01T10:30:00Z,Process Creation,suspicious_process.exe started,PID 1234",
            "2023-12-01T10:30:05Z,File Creation,Malicious file dropped,C:\\temp\\malware.exe",
            "2023-12-01T10:30:10Z,Registry Modification,Run key modified,Registry",
            "2023-12-01T10:30:15Z,Network Connection,Beacon to C&C server,Net logs"
        ]
        
        with open(timeline_path, 'w') as f:
            for entry in timeline_entries:
                f.write(entry + "\n")
        
        return timeline_path

# Example usage
pipeline = AutomatedForensicsPipeline("/tmp/forensics_test")

# Create a forensics case
case_id = pipeline.create_forensics_case("INC-20231201-001", "Malware infection on web server")
print(f"Created forensics case: {case_id}")

# Collect evidence
try:
    memory_dump = pipeline.collect_memory_dump(case_id, "web-server-001")
    print(f"Collected memory dump: {memory_dump}")
    
    disk_image = pipeline.collect_disk_image(case_id, "/dev/sda1", "web-server-001")
    print(f"Collected disk image: {disk_image}")
    
    network_logs = pipeline.collect_network_logs(case_id, ("2023-12-01T10:00:00Z", "2023-12-01T11:00:00Z"), "web-server-001")
    print(f"Collected network logs: {network_logs}")
    
    process_logs = pipeline.collect_process_logs(case_id, "web-server-001")
    print(f"Collected process logs: {process_logs}")
    
    # Run volatility analysis
    if 'volatility' in pipeline.tools:
        analysis = pipeline.run_volatility_analysis(case_id, memory_dump)
        print(f"Volatility analysis completed, findings: {len(analysis['findings'])}")
    
    # Generate timeline
    timeline = pipeline.generate_timeline(case_id)
    print(f"Generated timeline: {timeline}")
    
except Exception as e:
    print(f"Forensics collection failed: {str(e)}")

# Example: Incident response workflow engine
import asyncio
import json
from datetime import datetime
from typing import Dict, List, Any, Callable
from dataclasses import dataclass, field

@dataclass
class WorkflowStep:
    name: str
    action: Callable
    parameters: Dict[str, Any] = field(default_factory=dict)
    timeout: int = 300  # 5 minutes default timeout
    retry_count: int = 3
    dependencies: List[str] = field(default_factory=list)
    condition: Callable = lambda: True

@dataclass
class WorkflowExecution:
    workflow_id: str
    incident_id: str
    start_time: datetime
    steps: List[WorkflowStep]
    results: Dict[str, Any] = field(default_factory=dict)
    status: str = "running"
    error: str = None

class IncidentResponseWorkflowEngine:
    def __init__(self):
        self.workflows = {}
        self.workflow_templates = self.load_workflow_templates()
    
    def load_workflow_templates(self) -> Dict[str, List[WorkflowStep]]:
        """
        Load predefined workflow templates
        """
        return {
            'malware_response': [
                WorkflowStep(
                    name='contain_system',
                    action=self.contain_system_action,
                    parameters={'isolation_type': 'network_only'},
                    timeout=300,
                    dependencies=[]
                ),
                WorkflowStep(
                    name='collect_forensics',
                    action=self.collect_forensics_action,
                    parameters={'evidence_types': ['memory', 'disk', 'network']},
                    timeout=1800,
                    dependencies=['contain_system']
                ),
                WorkflowStep(
                    name='analyze_threat',
                    action=self.analyze_threat_action,
                    parameters={},
                    timeout=3600,
                    dependencies=['collect_forensics']
                ),
                WorkflowStep(
                    name='remediate_threat',
                    action=self.remediate_threat_action,
                    parameters={},
                    timeout=1800,
                    dependencies=['analyze_threat']
                ),
                WorkflowStep(
                    name='restore_system',
                    action=self.restore_system_action,
                    parameters={},
                    timeout=1800,
                    dependencies=['remediate_threat']
                )
            ],
            'data_breach_response': [
                WorkflowStep(
                    name='identify_scope',
                    action=self.identify_breach_scope_action,
                    parameters={},
                    timeout=1800,
                    dependencies=[]
                ),
                WorkflowStep(
                    name='preserve_evidence',
                    action=self.preserve_evidence_action,
                    parameters={},
                    timeout=3600,
                    dependencies=['identify_scope']
                ),
                WorkflowStep(
                    name='notify_authorities',
                    action=self.notify_authorities_action,
                    parameters={},
                    timeout=300,
                    dependencies=['preserve_evidence']
                ),
                WorkflowStep(
                    name='communicate_to_affected',
                    action=self.communicate_to_affected_action,
                    parameters={},
                    timeout=1800,
                    dependencies=['notify_authorities']
                ),
                WorkflowStep(
                    name='implement_controls',
                    action=self.implement_controls_action,
                    parameters={},
                    timeout=3600,
                    dependencies=['communicate_to_affected']
                )
            ],
            'ddos_response': [
                WorkflowStep(
                    name='activate_mitigation',
                    action=self.activate_mitigation_action,
                    parameters={'mitigation_type': 'rate_limiting'},
                    timeout=300,
                    dependencies=[]
                ),
                WorkflowStep(
                    name='monitor_attacks',
                    action=self.monitor_attacks_action,
                    parameters={},
                    timeout=3600,
                    dependencies=['activate_mitigation']
                ),
                WorkflowStep(
                    name='coordinate_with_isp',
                    action=self.coordinate_with_isp_action,
                    parameters={},
                    timeout=1800,
                    dependencies=['monitor_attacks']
                ),
                WorkflowStep(
                    name='document_incident',
                    action=self.document_incident_action,
                    parameters={},
                    timeout=1800,
                    dependencies=['coordinate_with_isp']
                )
            ]
        }
    
    def start_workflow(self, workflow_type: str, incident_id: str, parameters: Dict[str, Any] = None) -> str:
        """
        Start a new workflow execution
        """
        if workflow_type not in self.workflow_templates:
            raise ValueError(f"Unknown workflow type: {workflow_type}")
        
        workflow_id = f"WF-{datetime.utcnow().strftime('%Y%m%d-%H%M%S')}-{len(self.workflows):04d}"
        
        # Create workflow execution
        execution = WorkflowExecution(
            workflow_id=workflow_id,
            incident_id=incident_id,
            start_time=datetime.utcnow(),
            steps=self.workflow_templates[workflow_type].copy()
        )
        
        # Override parameters if provided
        if parameters:
            for step in execution.steps:
                if step.name in parameters:
                    step.parameters.update(parameters[step.name])
        
        self.workflows[workflow_id] = execution
        
        # Start workflow asynchronously
        asyncio.create_task(self.execute_workflow(execution))
        
        return workflow_id
    
    async def execute_workflow(self, execution: WorkflowExecution):
        """
        Execute workflow steps asynchronously
        """
        try:
            for step in execution.steps:
                # Check dependencies
                if not self.check_dependencies(execution, step):
                    execution.error = f"Dependencies not met for step: {step.name}"
                    execution.status = "failed"
                    break
                
                # Check condition
                if not step.condition():
                    print(f"Condition not met for step: {step.name}, skipping")
                    execution.results[step.name] = {'status': 'skipped', 'reason': 'condition_not_met'}
                    continue
                
                # Execute step
                result = await self.execute_step_with_retry(step, execution.incident_id)
                execution.results[step.name] = result
                
                if result['status'] != 'success':
                    execution.error = f"Step failed: {step.name}"
                    execution.status = "failed"
                    break
            
            if execution.status != "failed":
                execution.status = "completed"
        
        except Exception as e:
            execution.error = str(e)
            execution.status = "failed"
        
        execution.results['workflow_complete'] = {
            'status': execution.status,
            'completed_at': datetime.utcnow().isoformat(),
            'error': execution.error
        }
    
    def check_dependencies(self, execution: WorkflowExecution, step: WorkflowStep) -> bool:
        """
        Check if step dependencies are satisfied
        """
        for dep in step.dependencies:
            if dep not in execution.results:
                return False
            if execution.results[dep].get('status') != 'success':
                return False
        return True
    
    async def execute_step_with_retry(self, step: WorkflowStep, incident_id: str) -> Dict[str, Any]:
        """
        Execute a step with retry logic
        """
        for attempt in range(step.retry_count):
            try:
                # Execute the action
                result = await step.action(incident_id, **step.parameters)
                
                return {
                    'status': 'success',
                    'result': result,
                    'attempt': attempt + 1,
                    'executed_at': datetime.utcnow().isoformat()
                }
            
            except Exception as e:
                if attempt == step.retry_count - 1:
                    # Last attempt failed
                    return {
                        'status': 'failed',
                        'error': str(e),
                        'attempt': attempt + 1,
                        'executed_at': datetime.utcnow().isoformat()
                    }
                
                # Wait before retry
                await asyncio.sleep(2 ** attempt)  # Exponential backoff
    
    # Action implementations
    async def contain_system_action(self, incident_id: str, isolation_type: str = 'network_only') -> Dict[str, Any]:
        """
        Contain the affected system
        """
        # Simulate system containment
        await asyncio.sleep(2)  # Simulate API call delay
        
        return {
            'action': 'contain_system',
            'isolation_type': isolation_type,
            'systems_affected': ['web-server-001'],
            'isolation_status': 'complete'
        }
    
    async def collect_forensics_action(self, incident_id: str, evidence_types: List[str]) -> Dict[str, Any]:
        """
        Collect forensic evidence
        """
        # Simulate forensics collection
        await asyncio.sleep(5)  # Simulate collection time
        
        return {
            'action': 'collect_forensics',
            'evidence_types_collected': evidence_types,
            'evidence_locations': ['/forensics/evidence.zip'],
            'collection_status': 'complete'
        }
    
    async def analyze_threat_action(self, incident_id: str) -> Dict[str, Any]:
        """
        Analyze the collected threat
        """
        # Simulate threat analysis
        await asyncio.sleep(3)  # Simulate analysis time
        
        return {
            'action': 'analyze_threat',
            'threat_type': 'malware',
            'malware_family': 'trojan.generic',
            'indicators_of_compromise': ['192.168.1.100', 'suspicious.exe'],
            'analysis_status': 'complete'
        }
    
    async def remediate_threat_action(self, incident_id: str) -> Dict[str, Any]:
        """
        Remediate the threat
        """
        # Simulate threat remediation
        await asyncio.sleep(4)  # Simulate remediation time
        
        return {
            'action': 'remediate_threat',
            'remediation_actions': ['quarantine_file', 'terminate_process', 'reset_passwords'],
            'remediation_status': 'complete'
        }
    
    async def restore_system_action(self, incident_id: str) -> Dict[str, Any]:
        """
        Restore the system to normal operation
        """
        # Simulate system restoration
        await asyncio.sleep(3)  # Simulate restoration time
        
        return {
            'action': 'restore_system',
            'restoration_actions': ['remove_quarantine', 'verify_integrity', 'monitor_for_recurrence'],
            '