015_具身人工智能的安全防护体系：多层次防御与主动响应策略

具身AI安全防护体系：
┌───────────────┐   ┌───────────────┐   ┌───────────────┐
│ 预防层         │   │ 检测层         │   │ 响应层         │
│               │   │               │   │               │
│ •安全设计      │   │ •异常检测      │   │ •应急响应      │
│ •访问控制      │   │ •入侵检测      │   │ •恢复机制      │
│ •加密保护      │   │ •异常行为分析   │   │ •事后分析      │
└────────┬──────┘   └────────┬──────┘   └────────┬──────┘
         │                    │                    │
         ▼                    ▼                    ▼
┌─────────────────────────────────────────────────────────┐
│                    持续改进循环                           │
│                                                         │
│ •安全审计  •漏洞管理  •威胁情报  •安全更新              │
└─────────────────────────────────────────────────────────┘

# 环境安全监控系统
class EnvironmentalSecurityMonitor:
    def __init__(self):
        self.sensors = {
            'temperature': {'threshold': (0, 40), 'alert_level': 'warning', 'reading': None},
            'humidity': {'threshold': (20, 80), 'alert_level': 'warning', 'reading': None},
            'vibration': {'threshold': (0, 10), 'alert_level': 'warning', 'reading': None},
            'power_quality': {'threshold': (198, 242), 'alert_level': 'critical', 'reading': None},
            'motion': {'threshold': False, 'alert_level': 'warning', 'reading': None},
            'sound': {'threshold': (0, 70), 'alert_level': 'warning', 'reading': None}
        }
        self.alerts = []
        self.monitoring_active = True
    
    def collect_sensor_data(self):
        """收集环境传感器数据"""
        if not self.monitoring_active:
            return {'status': 'inactive', 'message': 'Monitoring is disabled'}
        
        # 在实际系统中，这里会从真实传感器获取数据
        # 模拟传感器数据采集
        for sensor_name in self.sensors:
            self.sensors[sensor_name]['reading'] = self._simulate_sensor_reading(sensor_name)
        
        return {'status': 'success', 'collected_at': datetime.now().isoformat()}
    
    def analyze_environmental_security(self):
        """分析环境安全状态，检测异常"""
        current_alerts = []
        security_status = 'normal'
        
        for sensor_name, sensor_data in self.sensors.items():
            if sensor_data['reading'] is None:
                continue
            
            # 检查传感器读数是否超出阈值
            is_breach, breach_type = self._check_threshold_breach(sensor_name, sensor_data)
            
            if is_breach:
                alert = self._generate_environmental_alert(
                    sensor_name,
                    sensor_data['reading'],
                    sensor_data['threshold'],
                    sensor_data['alert_level'],
                    breach_type
                )
                current_alerts.append(alert)
                
                # 更新整体安全状态
                if sensor_data['alert_level'] == 'critical':
                    security_status = 'critical'
                elif sensor_data['alert_level'] == 'warning' and security_status != 'critical':
                    security_status = 'warning'
        
        # 更新警报列表
        self.alerts = current_alerts
        
        return {
            'security_status': security_status,
            'alerts': current_alerts,
            'timestamp': datetime.now().isoformat(),
            'sensor_readings': {k: v['reading'] for k, v in self.sensors.items()}
        }
    
    def generate_physical_security_report(self):
        """生成物理安全状态报告"""
        # 确保已收集最新数据
        self.collect_sensor_data()
        analysis_result = self.analyze_environmental_security()
        
        # 生成综合报告
        report = {
            'report_id': f"PHYS-{datetime.now().strftime('%Y%m%d')}-{str(len(self.alerts)).zfill(4)}",
            'generated_at': datetime.now().isoformat(),
            'security_summary': analysis_result['security_status'],
            'active_alerts_count': len(analysis_result['alerts']),
            'sensor_health': self._assess_sensor_health(),
            'environmental_readings': analysis_result['sensor_readings'],
            'detailed_alerts': analysis_result['alerts'],
            'recommended_actions': self._generate_recommendations(analysis_result['security_status'])
        }
        
        return report
    
    def set_threshold(self, sensor_name, threshold):
        """设置传感器阈值"""
        if sensor_name not in self.sensors:
            return {'status': 'error', 'message': f'Sensor {sensor_name} not found'}
        
        self.sensors[sensor_name]['threshold'] = threshold
        return {'status': 'success', 'message': f'Threshold updated for {sensor_name}'}

# 安全通信框架
class SecureCommunicationFramework:
    def __init__(self, config):
        self.config = config
        self.certificates = {}
        self.active_connections = {}
        self.security_events = []
    
    def initialize_certificates(self):
        """初始化和加载通信证书"""
        # 加载或生成证书
        for component in self.config['components']:
            cert_path = component.get('cert_path')
            key_path = component.get('key_path')
            
            if cert_path and key_path:
                # 加载现有证书
                cert = self._load_certificate(cert_path, key_path)
            else:
                # 生成新证书
                cert = self._generate_certificate(component['name'])
            
            self.certificates[component['name']] = cert
        
        return {'status': 'success', 'message': 'Certificates initialized'}
    
    def establish_secure_connection(self, source, destination, connection_type):
        """建立安全连接"""
        # 验证源和目标组件
        if source not in self.certificates or destination not in self.certificates:
            return {'status': 'error', 'message': 'Invalid component(s)'}
        
        # 验证连接类型
        if connection_type not in self.config['allowed_connections']:
            return {'status': 'error', 'message': 'Connection type not allowed'}
        
        # 建立安全通道
        secure_channel = self._create_secure_channel(
            source,
            destination,
            self.certificates[source],
            self.certificates[destination]
        )
        
        # 记录连接
        connection_id = f"CONN-{datetime.now().strftime('%Y%m%d%H%M%S')}-{random.randint(1000, 9999)}"
        self.active_connections[connection_id] = {
            'source': source,
            'destination': destination,
            'type': connection_type,
            'channel': secure_channel,
            'established_at': datetime.now().isoformat(),
            'last_activity': datetime.now().isoformat()
        }
        
        # 记录安全事件
        self._log_security_event('connection_established', connection_id)
        
        return {
            'status': 'success',
            'connection_id': connection_id,
            'message': f'Secure connection established from {source} to {destination}'
        }
    
    def send_encrypted_message(self, connection_id, message):
        """发送加密消息"""
        # 验证连接
        if connection_id not in self.active_connections:
            return {'status': 'error', 'message': 'Connection not found'}
        
        connection = self.active_connections[connection_id]
        
        # 加密消息
        encrypted_message = self._encrypt_message(
            message,
            connection['channel']
        )
        
        # 添加消息完整性校验
        integrity_protected_message = self._add_integrity_protection(encrypted_message)
        
        # 发送消息
        send_result = self._transmit_message(
            connection['destination'],
            integrity_protected_message,
            connection['channel']
        )
        
        if send_result['status'] == 'success':
            # 更新连接活动时间
            connection['last_activity'] = datetime.now().isoformat()
            
            # 记录安全事件
            self._log_security_event('message_sent', connection_id)
        
        return send_result
    
    def verify_message_authenticity(self, message, sender_info):
        """验证消息的真实性和完整性"""
        # 检查发送者身份
        if sender_info['id'] not in self.certificates:
            return {'status': 'error', 'message': 'Unknown sender'}
        
        # 验证消息完整性
        integrity_result = self._verify_integrity(message, sender_info)
        if not integrity_result['valid']:
            return {
                'status': 'error', 
                'message': 'Integrity verification failed',
                'reason': integrity_result['reason']
            }
        
        # 解密消息
        decrypted_message = self._decrypt_message(
            message['content'],
            self.certificates[sender_info['id']]
        )
        
        return {
            'status': 'success',
            'authenticity': 'verified',
            'decrypted_message': decrypted_message,
            'sender': sender_info['id']
        }
    
    def monitor_network_security(self):
        """监控网络安全状态"""
        # 检查异常连接
        suspicious_connections = self._detect_suspicious_connections()
        
        # 检查通信异常
        communication_anomalies = self._detect_communication_anomalies()
        
        # 检查证书状态
        certificate_issues = self._check_certificate_status()
        
        # 生成安全报告
        security_report = {
            'report_id': f"NETSEC-{datetime.now().strftime('%Y%m%d')}-{str(len(self.security_events)).zfill(4)}",
            'timestamp': datetime.now().isoformat(),
            'active_connections': len(self.active_connections),
            'suspicious_connections': suspicious_connections,
            'communication_anomalies': communication_anomalies,
            'certificate_issues': certificate_issues,
            'security_recommendations': self._generate_security_recommendations(
                suspicious_connections,
                communication_anomalies,
                certificate_issues
            )
        }
        
        return security_report

# 模型安全防护框架
class ModelSecurityFramework:
    def __init__(self, model_registry):
        self.model_registry = model_registry
        self.security_checks = []
        self.model_versions = {}
    
    def register_model(self, model, model_info):
        """注册新模型，进行安全检查"""
        # 生成模型ID
        model_id = f"MODEL-{datetime.now().strftime('%Y%m%d')}-{str(len(self.model_versions)).zfill(4)}"
        
        # 进行模型安全检查
        security_assessment = self._perform_model_security_assessment(model, model_info)
        
        if security_assessment['status'] == 'failed':
            return {
                'status': 'error',
                'message': 'Model failed security assessment',
                'issues': security_assessment['issues']
            }
        
        # 为模型添加安全元数据
        secure_model = self._add_security_metadata(model, model_info, security_assessment)
        
        # 注册模型
        self.model_versions[model_id] = {
            'model': secure_model,
            'info': model_info,
            'security_assessment': security_assessment,
            'registered_at': datetime.now().isoformat(),
            'version': model_info.get('version', '1.0.0'),
            'status': 'active'
        }
        
        return {
            'status': 'success',
            'model_id': model_id,
            'security_score': security_assessment['score']
        }
    
    def verify_model_integrity(self, model_id, current_model_hash):
        """验证模型完整性"""
        if model_id not in self.model_versions:
            return {'status': 'error', 'message': 'Model not found'}
        
        registered_model = self.model_versions[model_id]
        expected_hash = self._calculate_model_hash(registered_model['model'])
        
        integrity_check = {
            'model_id': model_id,
            'expected_hash': expected_hash,
            'actual_hash': current_model_hash,
            'is_intact': expected_hash == current_model_hash,
            'verified_at': datetime.now().isoformat()
        }
        
        if not integrity_check['is_intact']:
            # 记录模型篡改事件
            self._log_model_tampering_event(model_id, expected_hash, current_model_hash)
        
        return integrity_check
    
    def protect_model_against_adversarial_attacks(self, model_id):
        """增强模型对抗对抗性攻击的能力"""
        if model_id not in self.model_versions:
            return {'status': 'error', 'message': 'Model not found'}
        
        model_info = self.model_versions[model_id]
        model = model_info['model']
        
        # 应用防御技术
        protected_model = self._apply_defensive_techniques(model, model_info['info']['task_type'])
        
        # 评估防御效果
        defense_evaluation = self._evaluate_defense_effectiveness(model, protected_model)
        
        # 更新模型
        updated_model_id = f"{model_id}-DEFENDED-{datetime.now().strftime('%Y%m%d%H%M')}"
        self.model_versions[updated_model_id] = {
            'model': protected_model,
            'info': model_info['info'],
            'security_assessment': defense_evaluation,
            'registered_at': datetime.now().isoformat(),
            'version': model_info['info'].get('version', '1.0.0') + '-defended',
            'status': 'active',
            'parent_model': model_id
        }
        
        return {
            'status': 'success',
            'protected_model_id': updated_model_id,
            'defense_effectiveness': defense_evaluation['effectiveness'],
            'defense_techniques_applied': defense_evaluation['techniques']
        }
    
    def detect_model_drift(self, model_id, new_data):
        """检测模型漂移，识别模型性能下降"""
        if model_id not in self.model_versions:
            return {'status': 'error', 'message': 'Model not found'}
        
        model = self.model_versions[model_id]['model']
        
        # 进行模型漂移检测
        drift_detection = self._perform_drift_detection(model, new_data)
        
        return drift_detection
    
    def generate_model_security_report(self, model_id):
        """生成模型安全报告"""
        if model_id not in self.model_versions:
            return {'status': 'error', 'message': 'Model not found'}
        
        model_info = self.model_versions[model_id]
        
        # 收集安全信息
        security_report = {
            'report_id': f"MODELSEC-{datetime.now().strftime('%Y%m%d')}-{model_id}",
            'generated_at': datetime.now().isoformat(),
            'model_id': model_id,
            'model_info': model_info['info'],
            'security_assessment': model_info['security_assessment'],
            'current_status': model_info['status'],
            'adversarial_robustness': self._assess_adversarial_robustness(model_id),
            'privacy_analysis': self._analyze_privacy_issues(model_id),
            'recommendations': self._generate_model_security_recommendations(model_info)
        }
        
        return security_report

# 实时异常检测系统
class RealTimeAnomalyDetector:
    def __init__(self, system_components):
        self.components = system_components
        self.baseline_behaviors = {}
        self.anomaly_detection_models = {}
        self.detection_history = []
    
    def establish_baseline(self, component_id, training_data, baseline_type='statistical'):
        """为系统组件建立行为基线"""
        if component_id not in self.components:
            return {'status': 'error', 'message': f'Component {component_id} not found'}
        
        # 根据基线类型选择建立方法
        if baseline_type == 'statistical':
            baseline = self._establish_statistical_baseline(training_data)
        elif baseline_type == 'ml_based':
            baseline = self._establish_ml_baseline(training_data)
        elif baseline_type == 'hybrid':
            baseline = self._establish_hybrid_baseline(training_data)
        else:
            return {'status': 'error', 'message': f'Unknown baseline type: {baseline_type}'}
        
        # 存储基线
        self.baseline_behaviors[component_id] = {
            'baseline': baseline,
            'type': baseline_type,
            'established_at': datetime.now().isoformat(),
            'training_data_size': len(training_data)
        }
        
        # 训练异常检测模型
        detection_model = self._train_detection_model(training_data, baseline)
        self.anomaly_detection_models[component_id] = detection_model
        
        return {
            'status': 'success',
            'component_id': component_id,
            'baseline_type': baseline_type,
            'baseline_quality': self._evaluate_baseline_quality(baseline, training_data)
        }
    
    def detect_anomalies(self, component_id, real_time_data):
        """检测实时数据流中的异常"""
        if component_id not in self.components:
            return {'status': 'error', 'message': f'Component {component_id} not found'}
        
        if component_id not in self.baseline_behaviors:
            return {'status': 'error', 'message': f'No baseline established for {component_id}'}
        
        baseline = self.baseline_behaviors[component_id]
        detection_model = self.anomaly_detection_models[component_id]
        
        # 执行异常检测
        anomalies = self._execute_anomaly_detection(
            real_time_data,
            baseline['baseline'],
            detection_model,
            baseline['type']
        )
        
        # 评估异常严重性
        for anomaly in anomalies:
            anomaly['severity'] = self._assess_anomaly_severity(anomaly, component_id)
            anomaly['timestamp'] = datetime.now().isoformat()
        
        # 记录检测结果
        detection_record = {
            'component_id': component_id,
            'timestamp': datetime.now().isoformat(),
            'data_points_analyzed': len(real_time_data),
            'anomalies_detected': len(anomalies),
            'anomalies': anomalies
        }
        
        self.detection_history.append(detection_record)
        
        return detection_record
    
    def correlate_anomalies(self, time_window='5m'):
        """跨组件关联异常，识别系统性威胁"""
        # 获取指定时间窗口内的检测记录
        recent_detections = self._get_recent_detections(time_window)
        
        # 按时间和类型对异常进行分组
        grouped_anomalies = self._group_anomalies_by_time_and_type(recent_detections)
        
        # 执行异常关联分析
        correlated_threats = self._identify_correlated_threats(grouped_anomalies)
        
        return {
            'correlation_time_window': time_window,
            'timestamp': datetime.now().isoformat(),
            'detections_analyzed': len(recent_detections),
            'correlated_threats': correlated_threats,
            'threat_assessment': self._assess_threats_severity(correlated_threats)
        }
    
    def update_detection_models(self, component_id, new_training_data):
        """更新异常检测模型，适应系统行为变化"""
        if component_id not in self.components:
            return {'status': 'error', 'message': f'Component {component_id} not found'}
        
        if component_id not in self.baseline_behaviors:
            return {'status': 'error', 'message': f'No baseline established for {component_id}'}
        
        baseline_info = self.baseline_behaviors[component_id]
        
        # 重新训练或更新检测模型
        updated_model = self._update_detection_model(
            self.anomaly_detection_models[component_id],
            new_training_data,
            baseline_info['baseline'],
            baseline_info['type']
        )
        
        # 更新模型
        self.anomaly_detection_models[component_id] = updated_model
        
        # 评估更新后的模型性能
        model_performance = self._evaluate_model_performance(
            updated_model,
            new_training_data,
            baseline_info['baseline']
        )
        
        return {
            'status': 'success',
            'component_id': component_id,
            'model_updated_at': datetime.now().isoformat(),
            'performance_metrics': model_performance
        }
    
    def generate_anomaly_detection_report(self, time_period='24h'):
        """生成异常检测报告"""
        # 获取指定时间段内的检测历史
        detection_period = self._get_detection_period(time_period)
        
        # 汇总异常检测统计
        anomaly_stats = self._aggregate_anomaly_statistics(detection_period)
        
        # 分析异常趋势
        anomaly_trends = self._analyze_anomaly_trends(detection_period)
        
        # 生成报告
        report = {
            'report_id': f"ANOMALY-{datetime.now().strftime('%Y%m%d')}-{str(len(self.detection_history)).zfill(4)}",
            'generated_at': datetime.now().isoformat(),
            'time_period': time_period,
            'summary_statistics': anomaly_stats,
            'component_performance': self._evaluate_component_performance(detection_period),
            'anomaly_trends': anomaly_trends,
            'recommendations': self._generate_detection_recommendations(anomaly_stats, anomaly_trends)
        }
        
        return report

# 安全响应策略引擎
class SecurityResponseEngine:
    def __init__(self, system_components):
        self.components = system_components
        self.response_policies = {}
        self.response_history = []
        self.active_incidents = {}
    
    def define_response_policy(self, event_type, severity, response_actions):
        """定义安全事件响应策略"""
        policy_id = f"POLICY-{event_type}-{severity}-{datetime.now().strftime('%Y%m%d%H%M')}"
        
        # 验证响应动作的有效性
        validated_actions = []
        for action in response_actions:
            if self._validate_response_action(action):
                validated_actions.append(action)
        
        # 存储响应策略
        self.response_policies[policy_id] = {
            'event_type': event_type,
            'severity': severity,
            'response_actions': validated_actions,
            'defined_at': datetime.now().isoformat(),
            'status': 'active'
        }
        
        return {
            'status': 'success',
            'policy_id': policy_id,
            'validated_actions': len(validated_actions),
            'event_type': event_type,
            'severity': severity
        }
    
    def process_security_event(self, security_event):
        """处理安全事件，执行相应的响应措施"""
        # 确定事件类型和严重性
        event_type = security_event['type']
        severity = self._assess_event_severity(security_event)
        
        # 创建安全事件记录
        incident_id = self._create_security_incident(security_event, event_type, severity)
        
        # 查找匹配的响应策略
        matching_policies = self._find_matching_policies(event_type, severity)
        
        # 执行响应措施
        response_results = []
        for policy in matching_policies:
            policy_actions = self.response_policies[policy]['response_actions']
            for action in policy_actions:
                action_result = self._execute_response_action(action, security_event, incident_id)
                response_results.append(action_result)
        
        # 更新事件状态
        self._update_incident_status(incident_id, 'responding', response_results)
        
        # 记录响应历史
        response_record = {
            'incident_id': incident_id,
            'event_type': event_type,
            'severity': severity,
            'response_timestamp': datetime.now().isoformat(),
            'response_actions': response_results,
            'matched_policies': matching_policies
        }
        
        self.response_history.append(response_record)
        
        return {
            'status': 'responding',
            'incident_id': incident_id,
            'response_actions_executed': len(response_results),
            'matched_policies': matching_policies
        }
    
    def escalate_security_incident(self, incident_id, escalation_level):
        """升级安全事件，执行更高级别的响应措施"""
        if incident_id not in self.active_incidents:
            return {'status': 'error', 'message': f'Incident {incident_id} not found'}
        
        incident = self.active_incidents[incident_id]
        
        # 验证升级级别
        if not self._validate_escalation_level(escalation_level, incident['severity']):
            return {'status': 'error', 'message': f'Invalid escalation level: {escalation_level}'}
        
        # 查找升级响应策略
        escalation_policies = self._find_escalation_policies(
            incident['event_type'], 
            escalation_level
        )
        
        # 执行升级响应措施
        escalation_results = []
        for policy in escalation_policies:
            policy_actions = self.response_policies[policy]['response_actions']
            for action in policy_actions:
                action_result = self._execute_response_action(action, incident['details'], incident_id)
                escalation_results.append(action_result)
        
        # 更新事件状态
        self._update_incident_status(incident_id, 'escalated', escalation_results)
        
        # 更新事件严重性
        incident['severity'] = escalation_level
        
        return {
            'status': 'escalated',
            'incident_id': incident_id,
            'new_severity_level': escalation_level,
            'escalation_actions_executed': len(escalation_results)
        }
    
    def resolve_security_incident(self, incident_id, resolution_details):
        """解决安全事件，关闭事件记录"""
        if incident_id not in self.active_incidents:
            return {'status': 'error', 'message': f'Incident {incident_id} not found'}
        
        incident = self.active_incidents[incident_id]
        
        # 验证系统状态是否安全
        if not self._verify_system_secure(incident['affected_components']):
            return {'status': 'error', 'message': 'System not in secure state, cannot resolve'}
        
        # 更新事件状态
        resolution_record = {
            'resolution_time': datetime.now().isoformat(),
            'resolution_details': resolution_details,
            'resolution_type': self._determine_resolution_type(resolution_details),
            'verified_secure': True
        }
        
        incident['status'] = 'resolved'
        incident['resolution'] = resolution_record
        incident['resolved_at'] = datetime.now().isoformat()
        
        # 从活跃事件中移除
        resolved_incident = self.active_incidents.pop(incident_id)
        
        # 记录事件总结
        self._log_incident_summary(resolved_incident)
        
        return {
            'status': 'resolved',
            'incident_id': incident_id,
            'resolution_time': resolution_record['resolution_time'],
            'incident_duration': self._calculate_incident_duration(resolved_incident)
        }
    
    def generate_incident_response_report(self, incident_id=None, time_period='7d'):
        """生成安全事件响应报告"""
        if incident_id:
            # 生成单个事件的报告
            if incident_id not in self.response_history:
                return {'status': 'error', 'message': f'Incident {incident_id} not found in history'}
            
            incident_data = self._retrieve_incident_data(incident_id)
            report = self._generate_single_incident_report(incident_data)
        else:
            # 生成时间段内的报告
            time_range = self._get_time_range(time_period)
            incidents_in_period = self._get_incidents_in_time_range(time_range)
            report = self._generate_periodic_incident_report(incidents_in_period, time_period)
        
        return report

# 自适应安全策略管理器
class AdaptiveSecurityPolicyManager:
    def __init__(self, system_state_monitor, threat_intelligence_source):
        self.system_state = system_state_monitor
        self.threat_intel = threat_intelligence_source
        self.security_policies = {}
        self.policy_history = []
        self.adaptation_rules = []
    
    def define_adaptation_rule(self, condition, policy_changes):
        """定义安全策略自适应规则"""
        rule_id = f"RULE-{datetime.now().strftime('%Y%m%d%H%M%S')}-{random.randint(1000, 9999)}"
        
        # 验证条件和策略变更
        validated_condition = self._validate_adaptation_condition(condition)
        validated_changes = self._validate_policy_changes(policy_changes)
        
        # 存储自适应规则
        self.adaptation_rules.append({
            'rule_id': rule_id,
            'condition': validated_condition,
            'policy_changes': validated_changes,
            'created_at': datetime.now().isoformat(),
            'status': 'active'
        })
        
        return {
            'status': 'success',
            'rule_id': rule_id,
            'message': 'Adaptation rule defined successfully'
        }
    
    def evaluate_adaptation_conditions(self):
        """评估所有自适应规则的条件，识别需要应用的规则"""
        applicable_rules = []
        
        # 获取当前系统状态
        current_state = self.system_state.get_current_state()
        
        # 获取最新威胁情报
        latest_threats = self.threat_intel.get_latest_threats()
        
        # 评估每个规则
        for rule in self.adaptation_rules:
            if rule['status'] != 'active':
                continue
            
            condition_met = self._evaluate_condition(
                rule['condition'],
                current_state,
                latest_threats
            )
            
            if condition_met:
                applicable_rules.append(rule)
        
        return {
            'timestamp': datetime.now().isoformat(),
            'system_state_summary': self._summarize_system_state(current_state),
            'relevant_threats': self._identify_relevant_threats(latest_threats, current_state),
            'applicable_rules': [r['rule_id'] for r in applicable_rules],
            'rules_details': applicable_rules
        }
    
    def adapt_security_policies(self):
        """根据适用规则调整安全策略"""
        # 评估适用规则
        evaluation_result = self.evaluate_adaptation_conditions()
        
        policy_changes = []
        
        # 应用每个适用规则
        for rule in evaluation_result['rules_details']:
            # 记录当前策略状态
            policy_snapshot = self._take_policy_snapshot()
            
            # 应用策略变更
            for change in rule['policy_changes']:
                change_result = self._apply_policy_change(change)
                if change_result['status'] == 'success':
                    policy_changes.append(change_result)
            
            # 记录策略适应历史
            self._log_policy_adaptation(
                rule['rule_id'],
                policy_snapshot,
                policy_changes,
                evaluation_result['system_state_summary'],
                evaluation_result['relevant_threats']
            )
        
        return {
            'status': 'completed',
            'adaptation_timestamp': datetime.now().isoformat(),
            'rules_applied': evaluation_result['applicable_rules'],
            'policy_changes_executed': len(policy_changes),
            'system_state': evaluation_result['system_state_summary']
        }
    
    def optimize_security_policies(self):
        """优化安全策略，平衡安全性和系统性能"""
        # 获取当前系统性能数据
        performance_data = self.system_state.get_performance_metrics()
        
        # 分析安全策略对性能的影响
        policy_impact = self._analyze_policy_performance_impact(performance_data)
        
        # 识别可优化的策略
        optimizable_policies = self._identify_optimizable_policies(policy_impact)
        
        # 应用优化
        optimization_results = []
        for policy in optimizable_policies:
            optimization = self._optimize_policy(policy, policy_impact[policy['policy_id']])
            if optimization['status'] == 'success':
                optimization_results.append(optimization)
        
        return {
            'status': 'completed',
            'optimization_timestamp': datetime.now().isoformat(),
            'policies_optimized': len(optimization_results),
            'performance_improvement': self._calculate_performance_improvement(optimization_results),
            'security_impact': self._assess_security_impact(optimization_results)
        }
    
    def generate_adaptation_report(self, time_period='7d'):
        """生成安全策略自适应报告"""
        # 获取指定时间段内的适应历史
        time_range = self._get_time_range(time_period)
        adaptation_history = self._get_adaptation_history(time_range)
        
        # 分析适应模式
        adaptation_patterns = self._analyze_adaptation_patterns(adaptation_history)
        
        # 评估适应有效性
        effectiveness = self._evaluate_adaptation_effectiveness(adaptation_history)
        
        # 生成报告
        report = {
            'report_id': f"ADAPT-{datetime.now().strftime('%Y%m%d')}-{str(len(self.policy_history)).zfill(4)}",
            'generated_at': datetime.now().isoformat(),
            'time_period': time_period,
            'adaptation_summary': {
                'total_adaptations': len(adaptation_history),
                'rules_triggered': self._count_rule_triggers(adaptation_history),
                'policies_changed': self._count_policy_changes(adaptation_history)
            },
            'adaptation_patterns': adaptation_patterns,
            'effectiveness_metrics': effectiveness,
            'recommendations': self._generate_adaptation_recommendations(adaptation_patterns, effectiveness)
        }
        
        return report

# 安全态势感知系统
class SecuritySituationAwarenessSystem:
    def __init__(self, data_sources):
        self.data_sources = data_sources
        self.situation_data = {}
        self.threat_intelligence = {}
        self.visualization_data = {}
    
    def collect_situation_data(self):
        """收集多源态势感知数据"""
        collected_data = {}
        
        for source_id, source_config in self.data_sources.items():
            try:
                # 从数据源收集数据
                source_data = self._collect_from_source(source_id, source_config)
                collected_data[source_id] = {
                    'data': source_data,
                    'timestamp': datetime.now().isoformat(),
                    'status': 'success'
                }
            except Exception as e:
                collected_data[source_id] = {
                    'error': str(e),
                    'timestamp': datetime.now().isoformat(),
                    'status': 'error'
                }
        
        # 更新态势数据
        self.situation_data = collected_data
        
        return {
            'status': 'completed',
            'collected_at': datetime.now().isoformat(),
            'sources_count': len(collected_data),
            'successful_sources': sum(1 for s in collected_data.values() if s['status'] == 'success')
        }
    
    def integrate_threat_intelligence(self):
        """整合威胁情报数据"""
        threat_intel = {
            'internal_threats': self._collect_internal_threats(),
            'external_threats': self._collect_external_threats(),
            'industry_alarms': self._collect_industry_alarms(),
            'vulnerability_data': self._collect_vulnerability_data()
        }
        
        # 整合和关联威胁情报
        integrated_intel = self._integrate_and_correlate_threats(threat_intel)
        
        # 更新威胁情报
        self.threat_intelligence = integrated_intel
        
        return {
            'status': 'completed',
            'integration_time': datetime.now().isoformat(),
            'threat_sources': len(threat_intel),
            'relevant_threats': len(integrated_intel['prioritized_threats'])
        }
    
    def assess_security_situation(self):
        """评估当前安全态势"""
        # 确保有最新数据
        self.collect_situation_data()
        self.integrate_threat_intelligence()
        
        # 分析系统状态
        system_status = self._analyze_system_status(self.situation_data)
        
        # 评估威胁级别
        threat_assessment = self._assess_threat_level(self.threat_intelligence, system_status)
        
        # 识别关键风险
        key_risks = self._identify_key_risks(system_status, threat_assessment)
        
        # 综合态势评估
        situation_assessment = {
            'timestamp': datetime.now().isoformat(),
            'overall_status': self._determine_overall_status(system_status, threat_assessment),
            'system_health': system_status,
            'threat_level': threat_assessment,
            'key_risks': key_risks,
            'risk_factors': self._analyze_risk_factors(key_risks)
        }
        
        # 更新可视化数据
        self.visualization_data = self._prepare_visualization_data(situation_assessment)
        
        return situation_assessment
    
    def predict_security_trends(self, time_horizon='24h'):
        """预测安全趋势，提前识别潜在风险"""
        # 获取历史态势数据
        historical_data = self._get_historical_situation_data()
        
        # 分析趋势模式
        trend_patterns = self._analyze_trend_patterns(historical_data)
        
        # 预测未来态势
        predicted_situation = self._predict_future_situation(
            historical_data,
            trend_patterns,
            time_horizon
        )
        
        # 识别潜在风险事件
        potential_events = self._identify_potential_risk_events(predicted_situation)
        
        return {
            'prediction_time': datetime.now().isoformat(),
            'time_horizon': time_horizon,
            'predicted_situation': predicted_situation,
            'potential_events': potential_events,
            'confidence_level': self._calculate_prediction_confidence(trend_patterns)
        }
    
    def generate_situation_report(self, report_type='current'):
        """生成安全态势报告"""
        if report_type == 'current':
            # 生成当前态势报告
            situation_assessment = self.assess_security_situation()
            report = self._generate_current_situation_report(situation_assessment)
        elif report_type == 'trend':
            # 生成趋势分析报告
            historical_data = self._get_historical_situation_data()
            report = self._generate_trend_analysis_report(historical_data)
        elif report_type == 'prediction':
            # 生成预测报告
            prediction = self.predict_security_trends()
            report = self._generate_prediction_report(prediction)
        else:
            return {'status': 'error', 'message': f'Unknown report type: {report_type}'}
        
        return report

# 安全日志管理系统
class SecurityLogManager:
    def __init__(self, log_config):
        self.config = log_config
        self.log_sources = {}
        self.log_storage = self._initialize_log_storage()
        self.log_processors = []
    
    def register_log_source(self, source_id, source_config):
        """注册日志源"""
        # 验证日志源配置
        validated_config = self._validate_source_config(source_config)
        
        # 注册日志源
        self.log_sources[source_id] = {
            'config': validated_config,
            'status': 'active',
            'registered_at': datetime.now().isoformat()
        }
        
        # 初始化日志收集器
        self._initialize_log_collector(source_id, validated_config)
        
        return {
            'status': 'success',
            'source_id': source_id,
            'message': 'Log source registered successfully'
        }
    
    def collect_logs(self):
        """收集所有注册日志源的日志"""
        collection_results = {}
        total_logs = 0
        
        for source_id, source_info in self.log_sources.items():
            if source_info['status'] != 'active':
                continue
            
            try:
                # 收集日志
                logs = self._collect_source_logs(source_id, source_info['config'])
                
                # 处理日志
                processed_logs = self._process_logs(logs, source_id)
                
                # 存储日志
                storage_result = self._store_logs(processed_logs, source_id)
                
                total_logs += len(processed_logs)
                collection_results[source_id] = {
                    'status': 'success',
                    'logs_collected': len(logs),
                    'logs_processed': len(processed_logs),
                    'storage_result': storage_result
                }
            except Exception as e:
                collection_results[source_id] = {
                    'status': 'error',
                    'error': str(e)
                }
        
        return {
            'collection_time': datetime.now().isoformat(),
            'sources_collected': len(collection_results),
            'total_logs_processed': total_logs,
            'source_results': collection_results
        }
    
    def search_logs(self, query_parameters):
        """搜索日志"""
        # 验证查询参数
        validated_query = self._validate_query_parameters(query_parameters)
        
        # 构建查询
        search_query = self._build_search_query(validated_query)
        
        # 执行搜索
        search_results = self._execute_log_search(search_query)
        
        # 分页和格式化结果
        formatted_results = self._format_search_results(
            search_results,
            validated_query.get('page', 1),
            validated_query.get('page_size', 100)
        )
        
        return {
            'search_time': datetime.now().isoformat(),
            'query': validated_query,
            'total_results': search_results['total_count'],
            'page': validated_query.get('page', 1),
            'page_size': validated_query.get('page_size', 100),
            'results': formatted_results
        }
    
    def analyze_logs(self, analysis_type, parameters):
        """分析日志，识别模式和异常"""
        # 验证分析参数
        validated_params = self._validate_analysis_parameters(analysis_type, parameters)
        
        # 根据分析类型执行不同的分析
        if analysis_type == 'pattern_detection':
            analysis_results = self._detect_log_patterns(validated_params)
        elif analysis_type == 'anomaly_detection':
            analysis_results = self._detect_log_anomalies(validated_params)
        elif analysis_type == 'correlation_analysis':
            analysis_results = self._correlate_log_events(validated_params)
        else:
            return {'status': 'error', 'message': f'Unknown analysis type: {analysis_type}'}
        
        return {
            'analysis_time': datetime.now().isoformat(),
            'analysis_type': analysis_type,
            'parameters': validated_params,
            'results': analysis_results
        }
    
    def generate_log_report(self, report_config):
        """生成日志分析报告"""
        # 验证报告配置
        validated_config = self._validate_report_config(report_config)
        
        # 获取报告所需的日志数据
        log_data = self._retrieve_report_logs(validated_config)
        
        # 分析日志数据
        report_analysis = self._analyze_report_logs(log_data, validated_config)
        
        # 生成报告内容
        report_content = self._generate_report_content(report_analysis, validated_config)
        
        # 格式化报告
        formatted_report = self._format_report(report_content, validated_config.get('format', 'json'))
        
        return {
            'report_id': f"LOGREP-{datetime.now().strftime('%Y%m%d')}-{random.randint(1000, 9999)}",
            'generated_at': datetime.now().isoformat(),
            'report_config': validated_config,
            'report': formatted_report
        }

医疗机器人安全防护实施路线：

阶段一：基础安全建设（1-2个月）
- 全面风险评估
- 建立基础安全策略
- 部署基本防护措施
- 安全意识培训

阶段二：纵深防御体系（2-3个月）
- 网络分段与隔离
- 加密通信实施
- 访问控制加固
- 异常检测系统部署

阶段三：主动防御能力（2-3个月）
- 自动化响应系统
- 安全态势感知平台
- 威胁情报整合
- 定期安全演练

阶段四：持续改进（持续进行）
- 安全评估与审计
- 威胁模型更新
- 安全措施优化
- 应急响应演练