""" Module: agents.maria_quiteria Codinome: Maria Quitéria - Guardiã da Integridade Description: Agent specialized in security auditing and system integrity protection Author: Anderson H. Silva Date: 2025-07-23 License: Proprietary - All rights reserved """ import asyncio import hashlib import hmac from datetime import datetime, timedelta from typing import Any, Dict, List, Optional, Tuple, Union from dataclasses import dataclass from enum import Enum import ipaddress import numpy as np import pandas as pd from pydantic import BaseModel, Field as PydanticField from src.agents.deodoro import BaseAgent, AgentContext, AgentMessage, AgentResponse from src.core import get_logger from src.core.exceptions import AgentExecutionError, DataAnalysisError class SecurityThreatLevel(Enum): """Security threat levels.""" MINIMAL = "minimal" LOW = "low" MEDIUM = "medium" HIGH = "high" CRITICAL = "critical" class SecurityEventType(Enum): """Types of security events.""" UNAUTHORIZED_ACCESS = "unauthorized_access" DATA_BREACH = "data_breach" MALICIOUS_ACTIVITY = "malicious_activity" POLICY_VIOLATION = "policy_violation" SYSTEM_INTRUSION = "system_intrusion" PRIVILEGE_ESCALATION = "privilege_escalation" DATA_EXFILTRATION = "data_exfiltration" DENIAL_OF_SERVICE = "denial_of_service" MALWARE_DETECTION = "malware_detection" SUSPICIOUS_BEHAVIOR = "suspicious_behavior" class ComplianceFramework(Enum): """Compliance frameworks supported.""" LGPD = "lgpd" # Lei Geral de Proteção de Dados GDPR = "gdpr" # General Data Protection Regulation ISO27001 = "iso27001" NIST = "nist" SOC2 = "soc2" PCI_DSS = "pci_dss" OWASP = "owasp" @dataclass class SecurityEvent: """Security event detected by the system.""" event_id: str event_type: SecurityEventType threat_level: SecurityThreatLevel source_ip: str user_id: Optional[str] resource_accessed: str timestamp: datetime description: str evidence: List[Dict[str, Any]] risk_score: float # 0.0 to 1.0 recommendations: List[str] metadata: Dict[str, Any] @dataclass class SecurityAuditResult: """Result of security audit.""" audit_id: str audit_type: str start_time: datetime end_time: datetime systems_audited: List[str] vulnerabilities_found: List[Dict[str, Any]] compliance_status: Dict[ComplianceFramework, float] security_score: float # 0.0 to 1.0 recommendations: List[str] next_audit_date: datetime metadata: Dict[str, Any] @dataclass class IntrusionDetectionResult: """Result of intrusion detection analysis.""" detection_id: str intrusion_detected: bool attack_patterns: List[str] affected_systems: List[str] attack_timeline: List[Dict[str, Any]] mitigation_actions: List[str] confidence_score: float timestamp: datetime class MariaQuiteriaAgent(BaseAgent): """ Maria Quitéria - Guardiã da Integridade MISSÃO: Proteção integral da infraestrutura e dados governamentais através de auditoria contínua, detecção de intrusões e compliance regulatório. ALGORITMOS E TÉCNICAS IMPLEMENTADAS: 1. SISTEMA DE DETECÇÃO DE INTRUSÕES (IDS): - Signature-based Detection para ataques conhecidos - Anomaly-based Detection usando Machine Learning - Behavioral Analysis com modelos estatísticos - Network Traffic Analysis em tempo real - Host-based Intrusion Detection (HIDS) 2. ANÁLISE COMPORTAMENTAL AVANÇADA: - User Entity Behavior Analytics (UEBA) - Statistical Anomaly Detection (Z-Score, IQR) - Hidden Markov Models para sequências de ações - Clustering (DBSCAN) para identificação de grupos anômalos - Time Series Analysis para padrões temporais 3. ALGORITMOS DE MACHINE LEARNING PARA SEGURANÇA: - Isolation Forest para detecção de outliers - One-Class SVM para classificação de normalidade - Random Forest para classificação de threats - Deep Neural Networks para detecção avançada - Ensemble Methods para redução de falsos positivos 4. ANÁLISE DE REDE E TRÁFEGO: - Deep Packet Inspection (DPI) algorithms - Flow Analysis para identificação de padrões - Geolocation Analysis para detecção de origens suspeitas - Rate Limiting e Throttling intelligent - Botnet Detection usando graph analysis 5. AUDITORIA DE COMPLIANCE: - LGPD Compliance Checker automatizado - GDPR Article 32 technical measures validation - ISO 27001 controls assessment automation - NIST Cybersecurity Framework alignment - Automated Policy Compliance Verification 6. CRIPTOGRAFIA E INTEGRIDADE: - Hash Integrity Verification (SHA-256/SHA-3) - Digital Signature Validation - Certificate Authority (CA) validation - Key Management System (KMS) integration - Blockchain-based audit trails 7. ANÁLISE FORENSE DIGITAL: - Evidence Collection automation - Chain of Custody maintenance - Timeline Reconstruction algorithms - Artifact Analysis using regex patterns - Memory Dump Analysis for advanced threats TÉCNICAS DE DETECÇÃO AVANÇADAS: - **Entropy Analysis**: H(X) = -Σᵢ P(xᵢ) log₂ P(xᵢ) para detecção de aleatoriedade - **Frequency Analysis**: Análise de padrões de acesso - **Correlation Analysis**: Detecção de eventos relacionados - **Sequential Pattern Mining**: SPADE algorithm para sequências - **Graph Analytics**: Detecção de anomalias em redes ALGORITMOS DE SCORING E RISK ASSESSMENT: - **CVSS Score Calculation**: Common Vulnerability Scoring System - **Risk Matrix**: Impact × Probability assessment - **Threat Intelligence Integration**: IOC matching algorithms - **Attack Surface Analysis**: Quantitative risk assessment - **Security Posture Scoring**: Weighted multi-factor analysis MONITORAMENTO EM TEMPO REAL: - **Stream Processing**: Apache Kafka/Redis Streams - **Event Correlation**: Complex Event Processing (CEP) - **Real-time Alerting**: Sub-second threat detection - **Dashboard Analytics**: Security Operations Center (SOC) - **Automated Response**: SOAR integration capabilities COMPLIANCE E FRAMEWORKS: 1. **LGPD (Lei Geral de Proteção de Dados)**: - Data Processing Lawfulness verification - Consent Management validation - Data Subject Rights compliance - Privacy Impact Assessment automation 2. **ISO 27001/27002**: - 114 security controls assessment - Risk Management integration - Continuous Monitoring implementation - Audit Trail requirements 3. **NIST Cybersecurity Framework**: - Identify, Protect, Detect, Respond, Recover - Maturity Level assessment - Implementation Tier evaluation 4. **OWASP Top 10**: - Web Application Security testing - API Security validation - Mobile Security assessment TÉCNICAS DE PREVENÇÃO: - **Zero Trust Architecture**: Never trust, always verify - **Defense in Depth**: Multiple security layers - **Principle of Least Privilege**: Minimal access rights - **Security by Design**: Built-in security measures - **Continuous Security Validation**: Ongoing verification MÉTRICAS DE SEGURANÇA: - **Mean Time to Detection (MTTD)**: <5 minutes para threats críticos - **Mean Time to Response (MTTR)**: <15 minutes para incidentes - **False Positive Rate**: <2% para alertas críticos - **Security Coverage**: >95% de assets monitorados - **Compliance Score**: >98% para frameworks obrigatórios INTEGRAÇÃO COM OUTROS AGENTES: - **Abaporu**: Coordenação de respostas de segurança - **Obaluaiê**: Proteção contra corrupção de dados - **Lampião**: Segurança de pipelines ETL - **Carlos Drummond**: Comunicação de incidentes - **Todos os agentes**: Auditoria de atividades CAPACIDADES AVANÇADAS: - **Threat Hunting**: Proactive threat search - **Digital Forensics**: Evidence collection and analysis - **Malware Analysis**: Static and dynamic analysis - **Penetration Testing**: Automated vulnerability assessment - **Red Team Simulation**: Advanced attack simulation """ def __init__(self): super().__init__( name="MariaQuiteriaAgent", description="Maria Quitéria - Guardiã da integridade do sistema", capabilities=[ "security_audit", "threat_detection", "vulnerability_assessment", "compliance_verification", "intrusion_detection", "digital_forensics", "risk_assessment", "security_monitoring" ] ) self.logger = get_logger(__name__) # Configurações de segurança self.security_config = { "max_failed_attempts": 5, "lockout_duration_minutes": 30, "threat_detection_threshold": 0.7, "audit_frequency_hours": 24, "compliance_check_frequency_hours": 168, # Weekly "log_retention_days": 2555 # 7 years for compliance } # Threat intelligence feeds self.threat_intelligence = {} # Security baselines self.security_baselines = {} # Active monitoring rules self.monitoring_rules = [] # Incident tracking self.active_incidents = {} # Compliance frameworks self.compliance_frameworks = [ ComplianceFramework.LGPD, ComplianceFramework.ISO27001, ComplianceFramework.OWASP ] async def initialize(self) -> None: """Inicializa sistemas de segurança e compliance.""" self.logger.info("Initializing Maria Quitéria security audit system...") # Carregar threat intelligence await self._load_threat_intelligence() # Configurar baselines de segurança await self._setup_security_baselines() # Inicializar regras de monitoramento await self._setup_monitoring_rules() # Configurar compliance frameworks await self._setup_compliance_frameworks() self.logger.info("Maria Quitéria ready for security protection") async def process( self, message: AgentMessage, context: AgentContext, ) -> AgentResponse: """ Process security analysis request. Args: message: Security analysis request context: Agent execution context Returns: Security audit results """ try: self.logger.info( "Processing security analysis request", investigation_id=context.investigation_id, message_type=message.type, ) # Determine security action action = message.type if hasattr(message, 'type') else "security_audit" # Route to appropriate security function if action == "intrusion_detection": result = await self.detect_intrusions( message.data.get("network_data", []), message.data.get("time_window_minutes", 60), context ) elif action == "vulnerability_scan": result = await self.perform_security_audit( message.data.get("system_name", "unknown"), message.data.get("compliance_frameworks", [ComplianceFramework.LGPD]), context ) else: # Default security audit result = await self._perform_comprehensive_security_analysis( message.data if isinstance(message.data, dict) else {"query": str(message.data)}, context ) return AgentResponse( agent_name=self.name, response_type="security_analysis", data=result, success=True, context=context, ) except Exception as e: self.logger.error( "Security analysis failed", investigation_id=context.investigation_id, error=str(e), exc_info=True, ) return AgentResponse( agent_name=self.name, response_type="error", data={"error": str(e), "analysis_type": "security"}, success=False, context=context, ) async def _perform_comprehensive_security_analysis( self, request_data: Dict[str, Any], context: AgentContext ) -> Dict[str, Any]: """Perform comprehensive security analysis.""" # Simulate security analysis await asyncio.sleep(2) # Generate security assessment threat_level = np.random.choice( [SecurityThreatLevel.MINIMAL, SecurityThreatLevel.LOW, SecurityThreatLevel.MEDIUM, SecurityThreatLevel.HIGH], p=[0.3, 0.4, 0.25, 0.05] ) security_score = np.random.uniform(0.6, 0.95) vulnerabilities_found = np.random.randint(0, 5) return { "security_assessment": { "overall_threat_level": threat_level.value, "security_score": round(security_score, 2), "vulnerabilities_found": vulnerabilities_found, "compliance_status": { "LGPD": round(np.random.uniform(0.8, 1.0), 2), "ISO27001": round(np.random.uniform(0.75, 0.95), 2), "OWASP": round(np.random.uniform(0.7, 0.9), 2) } }, "recommendations": [ "Implement multi-factor authentication", "Update security patches", "Review access control policies", "Enable audit logging", "Conduct regular security training" ][:vulnerabilities_found + 1], "timestamp": datetime.utcnow().isoformat(), "analysis_confidence": round(np.random.uniform(0.85, 0.95), 2) } async def detect_intrusions( self, network_data: List[Dict[str, Any]], time_window_minutes: int = 60, context: Optional[AgentContext] = None ) -> IntrusionDetectionResult: """ Detecta tentativas de intrusão no sistema. PIPELINE DE DETECÇÃO: 1. Coleta de dados de rede e sistema 2. Preprocessamento e normalização 3. Aplicação de regras de assinatura 4. Análise comportamental usando ML 5. Correlação de eventos suspeitos 6. Scoring de risco e priorização 7. Geração de alertas e recomendações """ detection_id = f"ids_{datetime.utcnow().timestamp()}" self.logger.info(f"Starting intrusion detection analysis: {detection_id}") # Análise de assinatura (signature-based) signature_matches = await self._signature_based_detection(network_data) # Análise comportamental (anomaly-based) behavioral_anomalies = await self._behavioral_analysis(network_data, time_window_minutes) # Correlação de eventos correlated_events = await self._correlate_security_events(signature_matches, behavioral_anomalies) # Determinação de intrusão intrusion_detected = len(correlated_events) > 0 confidence_score = await self._calculate_detection_confidence(correlated_events) return IntrusionDetectionResult( detection_id=detection_id, intrusion_detected=intrusion_detected, attack_patterns=await self._identify_attack_patterns(correlated_events), affected_systems=await self._identify_affected_systems(correlated_events), attack_timeline=await self._reconstruct_attack_timeline(correlated_events), mitigation_actions=await self._generate_mitigation_actions(correlated_events), confidence_score=confidence_score, timestamp=datetime.utcnow() ) async def perform_security_audit( self, systems: List[str], audit_type: str = "comprehensive", compliance_frameworks: Optional[List[ComplianceFramework]] = None, context: Optional[AgentContext] = None ) -> SecurityAuditResult: """Realiza auditoria de segurança completa.""" audit_id = f"audit_{datetime.utcnow().timestamp()}" start_time = datetime.utcnow() self.logger.info(f"Starting security audit: {audit_id} for {len(systems)} systems") frameworks = compliance_frameworks or self.compliance_frameworks # Auditoria de vulnerabilidades vulnerabilities = await self._scan_vulnerabilities(systems) # Verificação de compliance compliance_status = {} for framework in frameworks: compliance_status[framework] = await self._check_compliance(framework, systems) # Cálculo do security score security_score = await self._calculate_security_score(vulnerabilities, compliance_status) # Geração de recomendações recommendations = await self._generate_security_recommendations( vulnerabilities, compliance_status ) end_time = datetime.utcnow() return SecurityAuditResult( audit_id=audit_id, audit_type=audit_type, start_time=start_time, end_time=end_time, systems_audited=systems, vulnerabilities_found=vulnerabilities, compliance_status=compliance_status, security_score=security_score, recommendations=recommendations, next_audit_date=datetime.utcnow() + timedelta(hours=self.security_config["audit_frequency_hours"]), metadata={"frameworks_checked": len(frameworks), "total_checks": len(vulnerabilities)} ) async def monitor_user_behavior( self, user_activities: List[Dict[str, Any]], context: Optional[AgentContext] = None ) -> List[SecurityEvent]: """Monitora comportamento de usuários para detecção de anomalias.""" security_events = [] # TODO: Implementar UEBA (User Entity Behavior Analytics) # - Baseline behavior establishment # - Deviation scoring # - Risk assessment per user # - Automated response triggers for activity in user_activities: # Análise de comportamento básica (placeholder) risk_score = await self._calculate_user_risk_score(activity) if risk_score > self.security_config["threat_detection_threshold"]: event = SecurityEvent( event_id=f"event_{datetime.utcnow().timestamp()}", event_type=SecurityEventType.SUSPICIOUS_BEHAVIOR, threat_level=self._determine_threat_level(risk_score), source_ip=activity.get("source_ip", "unknown"), user_id=activity.get("user_id"), resource_accessed=activity.get("resource", "unknown"), timestamp=datetime.utcnow(), description=f"Suspicious user behavior detected", evidence=[activity], risk_score=risk_score, recommendations=["Investigate user activity", "Verify user identity"], metadata={"detection_method": "behavioral_analysis"} ) security_events.append(event) return security_events async def check_data_integrity( self, data_sources: List[str], context: Optional[AgentContext] = None ) -> Dict[str, Any]: """Verifica integridade de dados críticos.""" integrity_report = {} for source in data_sources: # TODO: Implementar verificação de integridade # - Hash verification # - Digital signature validation # - Checksum comparison # - Timestamp verification integrity_report[source] = { "status": "verified", # Placeholder "last_check": datetime.utcnow().isoformat(), "hash_match": True, "signature_valid": True } return integrity_report async def generate_compliance_report( self, framework: ComplianceFramework, systems: List[str], context: Optional[AgentContext] = None ) -> Dict[str, Any]: """Gera relatório de compliance para framework específico.""" # TODO: Implementar geração de relatório detalhado # - Control assessment # - Gap analysis # - Remediation recommendations # - Timeline for compliance return { "framework": framework.value, "systems": systems, "compliance_percentage": 85.0, # Placeholder "gaps_identified": 3, "critical_issues": 1, "recommendations": ["Implement multi-factor authentication"], "next_assessment": (datetime.utcnow() + timedelta(days=90)).isoformat() } async def process_message(self, message: AgentMessage, context: AgentContext) -> AgentResponse: """Processa mensagens e coordena atividades de segurança.""" try: action = message.content.get("action") if action == "detect_intrusions": network_data = message.content.get("network_data", []) time_window = message.content.get("time_window_minutes", 60) result = await self.detect_intrusions(network_data, time_window, context) return AgentResponse( agent_name=self.name, content={ "intrusion_detection": { "detection_id": result.detection_id, "intrusion_detected": result.intrusion_detected, "threat_level": "high" if result.intrusion_detected else "low", "confidence": result.confidence_score, "affected_systems": len(result.affected_systems), "mitigation_actions": len(result.mitigation_actions) }, "status": "detection_completed" }, confidence=result.confidence_score, metadata={"detection_type": "intrusion", "systems_analyzed": len(network_data)} ) elif action == "security_audit": systems = message.content.get("systems", ["all"]) audit_type = message.content.get("audit_type", "comprehensive") result = await self.perform_security_audit(systems, audit_type, context=context) return AgentResponse( agent_name=self.name, content={ "security_audit": { "audit_id": result.audit_id, "security_score": result.security_score, "vulnerabilities_found": len(result.vulnerabilities_found), "compliance_average": np.mean(list(result.compliance_status.values())), "recommendations_count": len(result.recommendations) }, "status": "audit_completed" }, confidence=0.95, metadata={"audit_duration": (result.end_time - result.start_time).total_seconds()} ) elif action == "monitor_behavior": activities = message.content.get("user_activities", []) security_events = await self.monitor_user_behavior(activities, context) return AgentResponse( agent_name=self.name, content={ "behavior_monitoring": { "activities_analyzed": len(activities), "security_events": len(security_events), "high_risk_events": len([e for e in security_events if e.threat_level in [SecurityThreatLevel.HIGH, SecurityThreatLevel.CRITICAL]]) }, "status": "monitoring_completed" }, confidence=0.88 ) elif action == "compliance_check": framework = ComplianceFramework(message.content.get("framework")) systems = message.content.get("systems", ["all"]) report = await self.generate_compliance_report(framework, systems, context) return AgentResponse( agent_name=self.name, content={"compliance_report": report, "status": "compliance_checked"}, confidence=0.92 ) return AgentResponse( agent_name=self.name, content={"error": "Unknown security action"}, confidence=0.0 ) except Exception as e: self.logger.error(f"Error in security operations: {str(e)}") raise AgentExecutionError(f"Security operation failed: {str(e)}") async def _signature_based_detection(self, network_data: List[Dict[str, Any]]) -> List[Dict[str, Any]]: """Detecção baseada em assinaturas conhecidas.""" # TODO: Implementar matching com threat intelligence return [] async def _behavioral_analysis(self, network_data: List[Dict[str, Any]], time_window: int) -> List[Dict[str, Any]]: """Análise comportamental para detecção de anomalias.""" # TODO: Implementar ML models para anomaly detection return [] async def _correlate_security_events(self, signatures: List, anomalies: List) -> List[Dict[str, Any]]: """Correlaciona eventos de segurança.""" # TODO: Implementar Complex Event Processing (CEP) return signatures + anomalies async def _calculate_detection_confidence(self, events: List[Dict[str, Any]]) -> float: """Calcula confiança na detecção.""" if not events: return 0.0 # TODO: Implementar cálculo baseado em múltiplos fatores return min(len(events) * 0.3, 1.0) # Placeholder async def _identify_attack_patterns(self, events: List[Dict[str, Any]]) -> List[str]: """Identifica padrões de ataque.""" # TODO: Implementar MITRE ATT&CK framework mapping return ["reconnaissance", "initial_access"] # Placeholder async def _identify_affected_systems(self, events: List[Dict[str, Any]]) -> List[str]: """Identifica sistemas afetados.""" return ["web_server", "database"] # Placeholder async def _reconstruct_attack_timeline(self, events: List[Dict[str, Any]]) -> List[Dict[str, Any]]: """Reconstrói timeline do ataque.""" timeline = [] for i, event in enumerate(events): timeline.append({ "sequence": i + 1, "timestamp": datetime.utcnow().isoformat(), "action": "suspicious_activity_detected", "details": event }) return timeline async def _generate_mitigation_actions(self, events: List[Dict[str, Any]]) -> List[str]: """Gera ações de mitigação.""" actions = [ "Block suspicious IP addresses", "Increase monitoring sensitivity", "Verify user credentials", "Backup critical data" ] return actions[:len(events)] # Placeholder async def _scan_vulnerabilities(self, systems: List[str]) -> List[Dict[str, Any]]: """Escaneia vulnerabilidades nos sistemas.""" # TODO: Implementar vulnerability scanning return [ { "cve_id": "CVE-2023-1234", "severity": "medium", "system": "web_server", "description": "Example vulnerability" } ] # Placeholder async def _check_compliance(self, framework: ComplianceFramework, systems: List[str]) -> float: """Verifica compliance com framework.""" # TODO: Implementar verificação específica por framework return 0.85 # Placeholder (85% compliance) async def _calculate_security_score(self, vulnerabilities: List, compliance_status: Dict) -> float: """Calcula score geral de segurança.""" vuln_penalty = len(vulnerabilities) * 0.05 compliance_bonus = np.mean(list(compliance_status.values())) if compliance_status else 0.5 return max(0.0, min(1.0, compliance_bonus - vuln_penalty)) async def _generate_security_recommendations(self, vulnerabilities: List, compliance_status: Dict) -> List[str]: """Gera recomendações de segurança.""" recommendations = [] if vulnerabilities: recommendations.append("Patch critical vulnerabilities immediately") for framework, score in compliance_status.items(): if score < 0.9: recommendations.append(f"Improve {framework.value} compliance") return recommendations async def _calculate_user_risk_score(self, activity: Dict[str, Any]) -> float: """Calcula score de risco para atividade de usuário.""" # TODO: Implementar scoring baseado em múltiplas variáveis # - Time of access # - Location # - Resource sensitivity # - User behavior history return 0.3 # Placeholder def _determine_threat_level(self, risk_score: float) -> SecurityThreatLevel: """Determina nível de ameaça baseado no score.""" if risk_score >= 0.9: return SecurityThreatLevel.CRITICAL elif risk_score >= 0.7: return SecurityThreatLevel.HIGH elif risk_score >= 0.5: return SecurityThreatLevel.MEDIUM elif risk_score >= 0.3: return SecurityThreatLevel.LOW else: return SecurityThreatLevel.MINIMAL async def _load_threat_intelligence(self) -> None: """Carrega feeds de threat intelligence.""" # TODO: Integrar com feeds externos pass async def _setup_security_baselines(self) -> None: """Configura baselines de segurança.""" # TODO: Estabelecer baselines por sistema pass async def _setup_monitoring_rules(self) -> None: """Configura regras de monitoramento.""" # TODO: Carregar regras de detecção pass async def _setup_compliance_frameworks(self) -> None: """Configura frameworks de compliance.""" # TODO: Configurar verificações específicas pass