# Encrypted channel threat analysis framework
class EncryptedChannelThreatAnalyzer:
    def __init__(self):
        self.attack_vectors = {
            'protocol_downgrade': ProtocolDowngradeDetector(),
            'certificate_manipulation': CertificateManipulationDetector(),
            'side_channel': SideChannelAttackDetector(),
            'traffic_analysis': TrafficAnalysisDetector(),
            'implementation_flaws': ImplementationFlawDetector()
        }
        self.ml_analyzer = MLThreatAnalyzer()
        self.threat_intelligence = ThreatIntelligenceService()
    
    def analyze_encrypted_traffic(self, traffic_data, connection_metadata):
        """Comprehensive analysis of encrypted traffic for attack indicators"""
        threat_analysis = {
            'analysis_id': str(uuid.uuid4()),
            'timestamp': datetime.utcnow(),
            'connection_id': connection_metadata['connection_id'],
            'protocol_analysis': {},
            'attack_indicators': [],
            'risk_assessment': {},
            'recommended_actions': []
        }
        
        # Analyze each attack vector
        for vector_name, detector in self.attack_vectors.items():
            detection_result = detector.analyze(traffic_data, connection_metadata)
            threat_analysis['protocol_analysis'][vector_name] = detection_result
            
            if detection_result['threat_detected']:
                threat_analysis['attack_indicators'].append({
                    'attack_type': vector_name,
                    'confidence': detection_result['confidence'],
                    'indicators': detection_result['indicators'],
                    'severity': detection_result['severity']
                })
        
        # ML-based anomaly detection
        ml_analysis = self.ml_analyzer.detect_encrypted_anomalies(
            traffic_data,
            connection_metadata
        )
        threat_analysis['ml_analysis'] = ml_analysis
        
        # Risk assessment
        risk_score = self.calculate_risk_score(
            threat_analysis['attack_indicators'],
            ml_analysis
        )
        threat_analysis['risk_assessment'] = {
            'risk_score': risk_score,
            'risk_level': self.determine_risk_level(risk_score),
            'contributing_factors': self.identify_risk_factors(threat_analysis)
        }
        
        # Generate recommendations
        recommendations = self.generate_defense_recommendations(threat_analysis)
        threat_analysis['recommended_actions'] = recommendations
        
        return threat_analysis

class TLSSecurityAnalyzer:
    def __init__(self):
        self.cipher_analyzer = CipherSuiteAnalyzer()
        self.certificate_validator = CertificateValidator()
        self.handshake_analyzer = HandshakeAnalyzer()
        self.version_checker = ProtocolVersionChecker()
    
    def detect_tls_attacks(self, tls_handshake_data):
        """Detect various TLS attack patterns"""
        tls_analysis = {
            'handshake_id': tls_handshake_data['handshake_id'],
            'client_ip': tls_handshake_data['client_ip'],
            'server_ip': tls_handshake_data['server_ip'],
            'analysis_timestamp': datetime.utcnow(),
            'version_analysis': {},
            'cipher_analysis': {},
            'certificate_analysis': {},
            'handshake_analysis': {},
            'attack_indicators': [],
            'security_score': 0
        }
        
        # Analyze TLS version negotiation
        version_analysis = self.version_checker.analyze_version_negotiation(
            tls_handshake_data['client_hello'],
            tls_handshake_data['server_hello']
        )
        tls_analysis['version_analysis'] = version_analysis
        
        # Detect version downgrade attacks
        if version_analysis['downgrade_detected']:
            tls_analysis['attack_indicators'].append({
                'attack_type': 'version_downgrade',
                'severity': 'HIGH',
                'details': version_analysis['downgrade_details']
            })
        
        # Analyze cipher suite selection
        cipher_analysis = self.cipher_analyzer.analyze_cipher_selection(
            tls_handshake_data['client_hello']['cipher_suites'],
            tls_handshake_data['server_hello']['selected_cipher']
        )
        tls_analysis['cipher_analysis'] = cipher_analysis
        
        # Detect weak cipher selection
        if cipher_analysis['weak_cipher_selected']:
            tls_analysis['attack_indicators'].append({
                'attack_type': 'weak_cipher_selection',
                'severity': 'MEDIUM',
                'details': cipher_analysis['weakness_details']
            })
        
        # Validate certificate chain
        certificate_analysis = self.certificate_validator.validate_certificate_chain(
            tls_handshake_data['certificate_chain']
        )
        tls_analysis['certificate_analysis'] = certificate_analysis
        
        # Detect certificate manipulation
        if not certificate_analysis['valid']:
            tls_analysis['attack_indicators'].append({
                'attack_type': 'certificate_manipulation',
                'severity': 'CRITICAL',
                'details': certificate_analysis['validation_errors']
            })
        
        # Analyze handshake timing and patterns
        handshake_analysis = self.handshake_analyzer.analyze_handshake_patterns(
            tls_handshake_data
        )
        tls_analysis['handshake_analysis'] = handshake_analysis
        
        # Calculate security score
        security_score = self.calculate_tls_security_score(tls_analysis)
        tls_analysis['security_score'] = security_score
        
        return tls_analysis
    
    def detect_beast_crime_attacks(self, encrypted_traffic):
        """Detect BEAST and CRIME attack patterns"""
        attack_detection = {
            'traffic_id': encrypted_traffic['id'],
            'analysis_timestamp': datetime.utcnow(),
            'beast_indicators': [],
            'crime_indicators': [],
            'attack_probability': 0
        }
        
        # BEAST attack detection (CBC mode exploitation)
        if encrypted_traffic['cipher_mode'] == 'CBC':
            beast_patterns = self.detect_beast_patterns(encrypted_traffic)
            if beast_patterns['suspicious_patterns']:
                attack_detection['beast_indicators'] = beast_patterns['patterns']
        
        # CRIME attack detection (compression-based)
        if encrypted_traffic['compression_enabled']:
            crime_patterns = self.detect_crime_patterns(encrypted_traffic)
            if crime_patterns['compression_anomalies']:
                attack_detection['crime_indicators'] = crime_patterns['anomalies']
        
        # Calculate attack probability
        attack_probability = self.calculate_attack_probability(
            attack_detection['beast_indicators'],
            attack_detection['crime_indicators']
        )
        attack_detection['attack_probability'] = attack_probability
        
        return attack_detection

class SideChannelDefenseSystem:
    def __init__(self):
        self.timing_analyzer = TimingAnalysisDetector()
        self.power_analyzer = PowerAnalysisDetector()
        self.cache_analyzer = CacheTimingDetector()
        self.acoustic_analyzer = AcousticAnalysisDetector()
        self.countermeasure_engine = CountermeasureEngine()
    
    def detect_side_channel_attacks(self, system_telemetry, crypto_operations):
        """Detect various side-channel attack attempts"""
        side_channel_analysis = {
            'analysis_id': str(uuid.uuid4()),
            'timestamp': datetime.utcnow(),
            'system_id': system_telemetry['system_id'],
            'timing_analysis': {},
            'power_analysis': {},
            'cache_analysis': {},
            'acoustic_analysis': {},
            'attack_indicators': [],
            'countermeasures_recommended': []
        }
        
        # Timing attack detection
        timing_analysis = self.timing_analyzer.analyze_timing_patterns(
            crypto_operations,
            system_telemetry['timing_data']
        )
        side_channel_analysis['timing_analysis'] = timing_analysis
        
        if timing_analysis['timing_attack_detected']:
            side_channel_analysis['attack_indicators'].append({
                'attack_type': 'timing_attack',
                'confidence': timing_analysis['confidence'],
                'attack_vector': timing_analysis['attack_vector'],
                'targeted_operations': timing_analysis['targeted_operations']
            })
        
        # Power analysis attack detection
        if 'power_consumption' in system_telemetry:
            power_analysis = self.power_analyzer.analyze_power_patterns(
                crypto_operations,
                system_telemetry['power_consumption']
            )
            side_channel_analysis['power_analysis'] = power_analysis
            
            if power_analysis['power_attack_detected']:
                side_channel_analysis['attack_indicators'].append({
                    'attack_type': 'power_analysis_attack',
                    'confidence': power_analysis['confidence'],
                    'attack_method': power_analysis['attack_method']
                })
        
        # Cache timing attack detection
        cache_analysis = self.cache_analyzer.analyze_cache_patterns(
            crypto_operations,
            system_telemetry['cache_metrics']
        )
        side_channel_analysis['cache_analysis'] = cache_analysis
        
        if cache_analysis['cache_attack_detected']:
            side_channel_analysis['attack_indicators'].append({
                'attack_type': 'cache_timing_attack',
                'confidence': cache_analysis['confidence'],
                'cache_lines_targeted': cache_analysis['targeted_cache_lines']
            })
        
        # Generate countermeasures
        if side_channel_analysis['attack_indicators']:
            countermeasures = self.countermeasure_engine.generate_countermeasures(
                side_channel_analysis['attack_indicators']
            )
            side_channel_analysis['countermeasures_recommended'] = countermeasures
        
        return side_channel_analysis
    
    def implement_timing_attack_countermeasures(self, crypto_implementation):
        """Implement countermeasures against timing attacks"""
        countermeasures = {
            'implementation_id': crypto_implementation['id'],
            'countermeasures_applied': [],
            'performance_impact': {},
            'security_improvement': {}
        }
        
        # Constant-time implementation
        if not crypto_implementation['constant_time']:
            constant_time_impl = self.implement_constant_time_operations(
                crypto_implementation
            )
            countermeasures['countermeasures_applied'].append({
                'type': 'constant_time_implementation',
                'details': constant_time_impl
            })
        
        # Blinding techniques
        blinding_impl = self.implement_blinding_techniques(crypto_implementation)
        countermeasures['countermeasures_applied'].append({
            'type': 'cryptographic_blinding',
            'details': blinding_impl
        })
        
        # Random delays
        random_delay_impl = self.implement_random_delays(crypto_implementation)
        countermeasures['countermeasures_applied'].append({
            'type': 'random_delays',
            'details': random_delay_impl
        })
        
        # Measure performance impact
        performance_impact = self.measure_performance_impact(
            crypto_implementation,
            countermeasures['countermeasures_applied']
        )
        countermeasures['performance_impact'] = performance_impact
        
        return countermeasures

class TrafficAnalysisDefenseSystem:
    def __init__(self):
        self.traffic_analyzer = TrafficPatternAnalyzer()
        self.metadata_protector = MetadataProtector()
        self.traffic_obfuscator = TrafficObfuscator()
        self.flow_analyzer = NetworkFlowAnalyzer()
    
    def protect_against_traffic_analysis(self, network_traffic, protection_level):
        """Implement comprehensive traffic analysis protection"""
        protection_result = {
            'protection_id': str(uuid.uuid4()),
            'timestamp': datetime.utcnow(),
            'original_traffic_stats': self.analyze_traffic_statistics(network_traffic),
            'protection_level': protection_level,
            'protection_techniques': [],
            'protected_traffic': None,
            'effectiveness_metrics': {}
        }
        
        protected_traffic = network_traffic.copy()
        
        # Packet size padding
        if protection_level in ['MEDIUM', 'HIGH', 'MAXIMUM']:
            padding_result = self.metadata_protector.apply_packet_padding(
                protected_traffic,
                protection_level
            )
            protection_result['protection_techniques'].append({
                'technique': 'packet_padding',
                'parameters': padding_result['parameters'],
                'overhead': padding_result['overhead']
            })
            protected_traffic = padding_result['padded_traffic']
        
        # Timing obfuscation
        if protection_level in ['HIGH', 'MAXIMUM']:
            timing_result = self.traffic_obfuscator.obfuscate_timing_patterns(
                protected_traffic,
                protection_level
            )
            protection_result['protection_techniques'].append({
                'technique': 'timing_obfuscation',
                'parameters': timing_result['parameters'],
                'delay_variance': timing_result['delay_variance']
            })
            protected_traffic = timing_result['obfuscated_traffic']
        
        # Dummy traffic injection
        if protection_level == 'MAXIMUM':
            dummy_result = self.traffic_obfuscator.inject_dummy_traffic(
                protected_traffic,
                protection_level
            )
            protection_result['protection_techniques'].append({
                'technique': 'dummy_traffic_injection',
                'parameters': dummy_result['parameters'],
                'traffic_increase': dummy_result['traffic_increase']
            })
            protected_traffic = dummy_result['enhanced_traffic']
        
        # Flow correlation protection
        flow_protection = self.flow_analyzer.protect_flow_correlation(
            protected_traffic,
            protection_level
        )
        protection_result['protection_techniques'].append({
            'technique': 'flow_correlation_protection',
            'parameters': flow_protection['parameters']
        })
        protected_traffic = flow_protection['protected_traffic']
        
        protection_result['protected_traffic'] = protected_traffic
        
        # Measure protection effectiveness
        effectiveness = self.measure_protection_effectiveness(
            network_traffic,
            protected_traffic,
            protection_result['protection_techniques']
        )
        protection_result['effectiveness_metrics'] = effectiveness
        
        return protection_result
    
    def detect_traffic_analysis_attempts(self, network_monitoring_data):
        """Detect attempts to perform traffic analysis"""
        detection_result = {
            'detection_id': str(uuid.uuid4()),
            'timestamp': datetime.utcnow(),
            'monitoring_period': network_monitoring_data['period'],
            'analysis_attempts': [],
            'suspicious_patterns': [],
            'threat_level': 'LOW'
        }
        
        # Detect unusual monitoring patterns
        monitoring_patterns = self.analyze_monitoring_patterns(
            network_monitoring_data
        )
        
        # Identify potential traffic analysis tools
        analysis_tools = self.identify_analysis_tools(
            network_monitoring_data['tool_signatures']
        )
        
        if analysis_tools['suspicious_tools']:
            detection_result['analysis_attempts'].extend([
                {
                    'tool': tool['name'],
                    'confidence': tool['confidence'],
                    'capabilities': tool['analysis_capabilities']
                }
                for tool in analysis_tools['suspicious_tools']
            ])
        
        # Detect correlation analysis attempts
        correlation_attempts = self.detect_correlation_analysis(
            network_monitoring_data['flow_data']
        )
        
        if correlation_attempts['correlation_detected']:
            detection_result['suspicious_patterns'].append({
                'pattern_type': 'flow_correlation_analysis',
                'confidence': correlation_attempts['confidence'],
                'targeted_flows': correlation_attempts['targeted_flows']
            })
        
        # Calculate threat level
        threat_level = self.calculate_traffic_analysis_threat_level(
            detection_result['analysis_attempts'],
            detection_result['suspicious_patterns']
        )
        detection_result['threat_level'] = threat_level
        
        return detection_result

class AdvancedCertificateSecuritySystem:
    def __init__(self):
        self.certificate_validator = AdvancedCertificateValidator()
        self.pinning_manager = CertificatePinningManager()
        self.transparency_monitor = CertificateTransparencyMonitor()
        self.revocation_checker = RevocationStatusChecker()
        self.threat_detector = CertificateThreatDetector()
    
    def comprehensive_certificate_validation(self, certificate_chain, connection_context):
        """Perform comprehensive certificate validation with advanced security checks"""
        validation_result = {
            'validation_id': str(uuid.uuid4()),
            'timestamp': datetime.utcnow(),
            'certificate_chain': certificate_chain,
            'connection_context': connection_context,
            'validation_checks': {},
            'security_warnings': [],
            'validation_status': 'PENDING'
        }
        
        # Basic certificate validation
        basic_validation = self.certificate_validator.validate_basic_properties(
            certificate_chain
        )
        validation_result['validation_checks']['basic_validation'] = basic_validation
        
        # Certificate pinning validation
        pinning_validation = self.pinning_manager.validate_certificate_pinning(
            certificate_chain[0],  # Leaf certificate
            connection_context['hostname']
        )
        validation_result['validation_checks']['pinning_validation'] = pinning_validation
        
        # Certificate Transparency validation
        ct_validation = self.transparency_monitor.validate_ct_logs(
            certificate_chain[0]
        )
        validation_result['validation_checks']['ct_validation'] = ct_validation
        
        # Revocation status check
        revocation_status = self.revocation_checker.check_revocation_status(
            certificate_chain
        )
        validation_result['validation_checks']['revocation_status'] = revocation_status
        
        # Threat detection
        threat_analysis = self.threat_detector.analyze_certificate_threats(
            certificate_chain,
            connection_context
        )
        validation_result['validation_checks']['threat_analysis'] = threat_analysis
        
        # Compile security warnings
        security_warnings = []
        
        if not basic_validation['valid']:
            security_warnings.extend(basic_validation['errors'])
        
        if pinning_validation['pinning_enabled'] and not pinning_validation['pin_valid']:
            security_warnings.append({
                'type': 'CERTIFICATE_PINNING_FAILURE',
                'severity': 'CRITICAL',
                'message': 'Certificate does not match pinned certificate'
            })
        
        if not ct_validation['in_ct_logs']:
            security_warnings.append({
                'type': 'CERTIFICATE_TRANSPARENCY_MISSING',
                'severity': 'MEDIUM',
                'message': 'Certificate not found in Certificate Transparency logs'
            })
        
        if revocation_status['revoked']:
            security_warnings.append({
                'type': 'CERTIFICATE_REVOKED',
                'severity': 'CRITICAL',
                'message': 'Certificate has been revoked'
            })
        
        if threat_analysis['threats_detected']:
            security_warnings.extend([
                {
                    'type': 'CERTIFICATE_THREAT',
                    'severity': threat['severity'],
                    'message': threat['description']
                }
                for threat in threat_analysis['threats']
            ])
        
        validation_result['security_warnings'] = security_warnings
        
        # Determine validation status
        critical_warnings = [w for w in security_warnings if w['severity'] == 'CRITICAL']
        if critical_warnings:
            validation_result['validation_status'] = 'FAILED'
        elif security_warnings:
            validation_result['validation_status'] = 'WARNING'
        else:
            validation_result['validation_status'] = 'VALID'
        
        return validation_result
    
    def implement_dynamic_certificate_pinning(self, hostname, certificate_history):
        """Implement dynamic certificate pinning with machine learning"""
        pinning_strategy = {
            'hostname': hostname,
            'implementation_timestamp': datetime.utcnow(),
            'pinning_type': 'DYNAMIC',
            'pin_configuration': {},
            'update_policy': {},
            'monitoring_configuration': {}
        }
        
        # Analyze certificate history for patterns
        certificate_analysis = self.analyze_certificate_patterns(certificate_history)
        
        # Determine optimal pinning strategy
        if certificate_analysis['stable_ca']:
            # Pin to CA public key
            pinning_strategy['pin_configuration'] = {
                'pin_type': 'CA_PUBLIC_KEY',
                'pinned_keys': certificate_analysis['stable_ca_keys'],
                'backup_pins': certificate_analysis['backup_ca_keys']
            }
        elif certificate_analysis['stable_intermediate']:
            # Pin to intermediate certificate
            pinning_strategy['pin_configuration'] = {
                'pin_type': 'INTERMEDIATE_CERTIFICATE',
                'pinned_keys': certificate_analysis['stable_intermediate_keys'],
                'backup_pins': certificate_analysis['backup_intermediate_keys']
            }
        else:
            # Pin to leaf certificate with frequent updates
            pinning_strategy['pin_configuration'] = {
                'pin_type': 'LEAF_CERTIFICATE',
                'pinned_keys': [certificate_history[-1]['public_key']],
                'update_frequency': 'WEEKLY'
            }
        
        # Configure automatic pin updates
        pinning_strategy['update_policy'] = {
            'update_mechanism': 'AUTOMATED',
            'validation_required': True,
            'rollback_capability': True,
            'update_notification': True
        }
        
        # Configure monitoring
        pinning_strategy['monitoring_configuration'] = {
            'pin_failure_alerting': True,
            'certificate_change_monitoring': True,
            'ct_log_monitoring': True,
            'threat_intelligence_integration': True
        }
        
        return pinning_strategy

class QuantumSafeTLSImplementation:
    def __init__(self):
        self.pq_key_exchange = PostQuantumKeyExchange()
        self.pq_signatures = PostQuantumSignatures()
        self.hybrid_crypto = HybridCryptographicSuite()
        self.quantum_rng = QuantumRandomNumberGenerator()
    
    def establish_quantum_safe_connection(self, client_context, server_context):
        """Establish quantum-safe TLS connection"""
        connection_establishment = {
            'connection_id': str(uuid.uuid4()),
            'timestamp': datetime.utcnow(),
            'quantum_safe': True,
            'handshake_data': {},
            'cryptographic_suite': {},
            'security_parameters': {},
            'connection_status': 'ESTABLISHING'
        }
        
        # Quantum-safe key exchange
        key_exchange_result = self.pq_key_exchange.perform_key_exchange(
            client_context['pq_public_key'],
            server_context['pq_public_key']
        )
        connection_establishment['handshake_data']['key_exchange'] = key_exchange_result
        
        # Hybrid cryptographic suite negotiation
        crypto_suite = self.hybrid_crypto.negotiate_cipher_suite(
            client_context['supported_suites'],
            server_context['supported_suites']
        )
        connection_establishment['cryptographic_suite'] = crypto_suite
        
        # Generate quantum-safe session keys
        session_keys = self.generate_quantum_safe_session_keys(
            key_exchange_result['shared_secret'],
            connection_establishment['connection_id']
        )
        connection_establishment['security_parameters']['session_keys'] = session_keys
        
        # Quantum-safe certificate validation
        cert_validation = self.validate_quantum_safe_certificates(
            server_context['certificate_chain']
        )
        connection_establishment['handshake_data']['certificate_validation'] = cert_validation
        
        if cert_validation['valid']:
            connection_establishment['connection_status'] = 'ESTABLISHED'
        else:
            connection_establishment['connection_status'] = 'FAILED'
            connection_establishment['failure_reason'] = cert_validation['errors']
        
        return connection_establishment
    
    def implement_quantum_safe_record_protocol(self, connection_params):
        """Implement quantum-safe record layer protocol"""
        record_protocol = {
            'protocol_version': 'TLS-PQ-1.0',
            'encryption_algorithm': connection_params['encryption_algorithm'],
            'authentication_algorithm': connection_params['authentication_algorithm'],
            'key_derivation': connection_params['key_derivation'],
            'record_processing': {}
        }
        
        # Configure quantum-safe encryption
        if connection_params['encryption_algorithm'] == 'HYBRID':
            record_protocol['record_processing']['encryption'] = {
                'classical_algorithm': 'AES-256-GCM',
                'post_quantum_algorithm': 'CRYSTALS-Kyber-1024',
                'combination_method': 'ENCRYPT_THEN_ENCRYPT'
            }
        
        # Configure quantum-safe authentication
        record_protocol['record_processing']['authentication'] = {
            'signature_algorithm': 'CRYSTALS-Dilithium-5',
            'hash_algorithm': 'SHA3-512',
            'mac_algorithm': 'HMAC-SHA3-512'
        }
        
        # Configure forward secrecy
        record_protocol['forward_secrecy'] = {
            'key_update_frequency': 'PER_MESSAGE',
            'quantum_safe_key_derivation': True,
            'perfect_forward_secrecy': True
        }
        
        return record_protocol

class EncryptedChannelMonitoringSystem:
    def __init__(self):
        self.traffic_monitor = EncryptedTrafficMonitor()
        self.anomaly_detector = EncryptedTrafficAnomalyDetector()
        self.threat_correlator = ThreatCorrelationEngine()
        self.incident_responder = IncidentResponseSystem()
        self.forensics_engine = DigitalForensicsEngine()
    
    def monitor_encrypted_channels(self, network_interfaces):
        """Comprehensive monitoring of encrypted communication channels"""
        monitoring_session = {
            'session_id': str(uuid.uuid4()),
            'start_timestamp': datetime.utcnow(),
            'monitored_interfaces': network_interfaces,
            'detected_anomalies': [],
            'security_incidents': [],
            'performance_metrics': {},
            'monitoring_status': 'ACTIVE'
        }
        
        for interface in network_interfaces:
            # Monitor encrypted traffic patterns
            traffic_analysis = self.traffic_monitor.analyze_interface_traffic(
                interface
            )
            
            # Detect anomalies in encrypted traffic
            anomalies = self.anomaly_detector.detect_anomalies(
                traffic_analysis
            )
            
            if anomalies['anomalies_detected']:
                monitoring_session['detected_anomalies'].extend(anomalies['anomalies'])
                
                # Correlate anomalies with threat intelligence
                threat_correlation = self.threat_correlator.correlate_threats(
                    anomalies['anomalies']
                )
                
                # Generate security incidents for high-confidence threats
                for threat in threat_correlation['correlated_threats']:
                    if threat['confidence'] > 0.8:
                        incident = self.generate_security_incident(
                            threat,
                            interface,
                            traffic_analysis
                        )
                        monitoring_session['security_incidents'].append(incident)
        
        # Update performance metrics
        monitoring_session['performance_metrics'] = self.calculate_monitoring_metrics(
            monitoring_session
        )
        
        return monitoring_session
    
    def respond_to_encrypted_channel_incident(self, incident_data):
        """Automated response to encrypted channel security incidents"""
        incident_response = {
            'incident_id': incident_data['incident_id'],
            'response_timestamp': datetime.utcnow(),
            'incident_type': incident_data['type'],
            'response_actions': [],
            'containment_measures': [],
            'forensic_analysis': {},
            'response_status': 'IN_PROGRESS'
        }
        
        # Immediate containment actions
        if incident_data['severity'] == 'CRITICAL':
            containment_actions = self.incident_responder.execute_immediate_containment(
                incident_data
            )
            incident_response['containment_measures'] = containment_actions
        
        # Forensic analysis of encrypted traffic
        forensic_analysis = self.forensics_engine.analyze_encrypted_incident(
            incident_data['traffic_data'],
            incident_data['metadata']
        )
        incident_response['forensic_analysis'] = forensic_analysis
        
        # Generate response recommendations
        response_recommendations = self.generate_response_recommendations(
            incident_data,
            forensic_analysis
        )
        incident_response['response_actions'] = response_recommendations
        
        incident_response['response_status'] = 'COMPLETED'
        
        return incident_response