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免费宣传网站,一件代发货源开网店货源,学校网站建设费计入什么科目,六安企业网站seo多少钱Python实现微信域名量子对抗性流量伪装与自适应防御系统功能概述本系统实现了一个基于量子计算、深度强化学习和联邦学习的微信域名智能对抗系统。通过量子特征编码、对抗性流量生成、自适应防御策略和多智能体协同#xff0c;构建了一个能够实时学习和适应微信风控系统的高级…Python实现微信域名量子对抗性流量伪装与自适应防御系统功能概述本系统实现了一个基于量子计算、深度强化学习和联邦学习的微信域名智能对抗系统。通过量子特征编码、对抗性流量生成、自适应防御策略和多智能体协同构建了一个能够实时学习和适应微信风控系统的高级对抗防御网络。#!/usr/bin/env python3 微信域名量子对抗性流量伪装与自适应防御系统 版本v9.0 功能量子特征编码、对抗性流量伪装、自适应防御策略、多智能体协同 import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F import numpy as np from typing import Dict, List, Tuple, Optional, Any, Callable import asyncio import aiohttp from aiohttp import ClientSession, TCPConnector import hashlib import time import json from datetime import datetime, timedelta from dataclasses import dataclass, field from enum import Enum, auto import logging from collections import deque, defaultdict import random import string import uuid import re import math import itertools from scipy import stats, signal, optimize import warnings warnings.filterwarnings(ignore) # 配置高级日志 logging.basicConfig( levellogging.INFO, format%(asctime)s - %(name)s - %(levelname)s - %(message)s, handlers[ logging.FileHandler(quantum_adversarial.log), logging.StreamHandler() ] ) logger logging.getLogger(__name__) # 高级数据结构定义 class DefenseStrategy(Enum): 防御策略枚举 STEALTH_MODE auto() # 隐身模式 EVASIVE_MODE auto() # 规避模式 AGGRESSIVE_MODE auto() # 激进模式 ADAPTIVE_MODE auto() # 自适应模式 MIMICRY_MODE auto() # 模仿模式 CONFUSION_MODE auto() # 混淆模式 DECOY_MODE auto() # 诱饵模式 QUANTUM_MODE auto() # 量子模式 class TrafficPattern(Enum): 流量模式枚举 HUMAN_LIKE auto() # 人类行为 BOT_MIMIC auto() # 机器人模仿 HYBRID auto() # 混合行为 BURST auto() # 突发流量 STEADY auto() # 稳定流量 RANDOM auto() # 随机流量 PATTERNED auto() # 模式化流量 ADAPTIVE auto() # 自适应流量 dataclass class QuantumFeature: 量子特征 superposition: np.ndarray entanglement_matrix: np.ndarray coherence_time: float decoherence_rate: float measurement_basis: str quantum_state: Dict[str, float] def encode_classical(self, data: np.ndarray) - np.ndarray: 将经典数据编码为量子特征 # 振幅编码 amplitudes data / np.linalg.norm(data) # 相位编码 phases np.angle(np.fft.fft(data)) # 量子态叠加 quantum_features np.concatenate([ amplitudes * np.exp(1j * phases), self.superposition ]) return quantum_features dataclass class DefenseState: 防御状态 domain: str risk_level: float defense_strategy: DefenseStrategy traffic_pattern: TrafficPattern success_rate: float response_time: float quantum_features: Optional[QuantumFeature] None temporal_features: Dict[str, float] field(default_factorydict) spatial_features: Dict[str, float] field(default_factorydict) behavioral_features: Dict[str, float] field(default_factorydict) def to_quantum_encoding(self) - np.ndarray: 转换为量子编码 # 基础特征 features np.array([ self.risk_level, self.success_rate, self.response_time, len(self.temporal_features) / 100.0, len(self.spatial_features) / 100.0, len(self.behavioral_features) / 100.0 ]) # 添加时间特征 temporal_vals list(self.temporal_features.values()) features np.concatenate([features, temporal_vals[:20]]) # 取前20个 # 添加空间特征 spatial_vals list(self.spatial_features.values()) features np.concatenate([features, spatial_vals[:20]]) # 取前20个 # 添加行为特征 behavioral_vals list(self.behavioral_features.values()) features np.concatenate([features, behavioral_vals[:20]]) # 取前20个 # 填充到固定长度 if len(features) 128: features np.pad(features, (0, 128 - len(features))) else: features features[:128] return features # 量子神经网络 class QuantumLayer(nn.Module): 量子层 - 模拟量子计算 def __init__(self, input_dim: int, output_dim: int, num_qubits: int 8): super().__init__() self.num_qubits num_qubits self.input_dim input_dim self.output_dim output_dim # 量子门参数 self.theta nn.Parameter(torch.randn(num_qubits, 3)) # 旋转门参数 self.phi nn.Parameter(torch.randn(num_qubits)) # 相位参数 self.entanglement_weights nn.Parameter(torch.randn(num_qubits, num_qubits)) # 编码器 self.encoder nn.Sequential( nn.Linear(input_dim, 256), nn.LayerNorm(256), nn.GELU(), nn.Dropout(0.3), nn.Linear(256, 128), nn.LayerNorm(128), nn.GELU(), nn.Dropout(0.3), nn.Linear(128, 2**num_qubits) ) # 解码器 self.decoder nn.Sequential( nn.Linear(2**num_qubits, 128), nn.LayerNorm(128), nn.GELU(), nn.Dropout(0.3), nn.Linear(128, 64), nn.LayerNorm(64), nn.GELU(), nn.Dropout(0.3), nn.Linear(64, output_dim) ) def forward(self, x: torch.Tensor) - torch.Tensor: # 经典到量子编码 quantum_state self._encode_to_quantum(x) # 量子门操作 quantum_state self._apply_quantum_gates(quantum_state) # 量子测量 measurements self._measure_quantum_state(quantum_state) # 量子到经典解码 output self.decoder(measurements) return output def _encode_to_quantum(self, x: torch.Tensor) - torch.Tensor: 编码到量子态 # 经典特征提取 encoded self.encoder(x) # 振幅编码 amplitudes F.softmax(encoded, dim-1) # 相位编码 phases torch.angle(torch.fft.fft(encoded)) # 创建量子态 quantum_state amplitudes * torch.exp(1j * phases) return quantum_state def _apply_quantum_gates(self, state: torch.Tensor) - torch.Tensor: 应用量子门 batch_size state.shape[0] num_states 2**self.num_qubits # 应用旋转门 for q in range(self.num_qubits): # 绕X轴旋转 rx self._rx_gate(self.theta[q, 0]) # 绕Y轴旋转 ry self._ry_gate(self.theta[q, 1]) # 绕Z轴旋转 rz self._rz_gate(self.theta[q, 2]) # 组合旋转 rotation rz ry rx # 应用旋转门到量子态 state self._apply_single_qubit_gate(state, q, rotation) # 应用纠缠门 for i in range(self.num_qubits): for j in range(i1, self.num_qubits): if abs(self.entanglement_weights[i, j]) 0.1: state self._apply_cnot_gate(state, i, j) return state def _measure_quantum_state(self, state: torch.Tensor) - torch.Tensor: 测量量子态 # 计算概率分布 probabilities torch.abs(state) ** 2 # 采样测量结果 measurements torch.multinomial(probabilities, 1).squeeze() # 转换为one-hot编码 one_hot F.one_hot(measurements, num_classes2**self.num_qubits).float() return one_hot # 对抗性流量生成器 class AdversarialTrafficGenerator: 对抗性流量生成器 def __init__(self): self.user_agents self._load_user_agents() self.behavior_profiles self._create_behavior_profiles() self.traffic_patterns self._create_traffic_patterns() self.ip_pool self._create_ip_pool() def _load_user_agents(self) - List[str]: 加载用户代理 ua_list [ Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36, Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36, Mozilla/5.0 (iPhone; CPU iPhone OS 14_0 like Mac OS X) AppleWebKit/605.1.15, Mozilla/5.0 (Linux; Android 10; SM-G973F) AppleWebKit/537.36, Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36, ] return ua_list def _create_behavior_profiles(self) - Dict[str, Dict[str, Any]]: 创建行为画像 return { casual_user: { click_density: 0.3, scroll_depth: (0.3, 0.7), dwell_time: (5, 20), attention_span: 0.5, interaction_intensity: 0.4 }, researcher: { click_density: 0.6, scroll_depth: (0.7, 0.9), dwell_time: (20, 60), attention_span: 0.8, interaction_intensity: 0.7 }, social_user: { click_density: 0.4, scroll_depth: (0.5, 0.8), dwell_time: (8, 30), attention_span: 0.6, interaction_intensity: 0.5 }, shopper: { click_density: 0.5, scroll_depth: (0.6, 0.9), dwell_time: (10, 40), attention_span: 0.7, interaction_intensity: 0.6 } } def generate_traffic(self, domain: str, pattern: TrafficPattern, duration: int 300) - List[Dict[str, Any]]: 生成对抗性流量 traffic [] start_time time.time() session_id hashlib.md5(f{domain}_{time.time()}.encode()).hexdigest()[:16] while time.time() - start_time duration: request self._generate_request( domaindomain, patternpattern, session_idsession_id ) traffic.append(request) # 智能间隔 interval self._calculate_interval(pattern, len(traffic)) time.sleep(interval) return traffic def _generate_request(self, domain: str, pattern: TrafficPattern, session_id: str) - Dict[str, Any]: 生成单个请求 # 随机选择请求类型 request_types [page_view, click, scroll, ajax, form_submit] weights [0.5, 0.2, 0.15, 0.1, 0.05] request_type random.choices(request_types, weightsweights)[0] # 生成请求 request { timestamp: datetime.now().isoformat(), session_id: session_id, domain: domain, request_type: request_type, user_agent: random.choice(self.user_agents), ip_address: random.choice(self.ip_pool), headers: self._generate_headers(), cookies: self._generate_cookies(), referrer: self._generate_referrer(domain), behavior_metrics: self._generate_behavior_metrics(pattern) } return request def _calculate_interval(self, pattern: TrafficPattern, request_count: int) - float: 计算请求间隔 if pattern TrafficPattern.BURST: # 突发模式快速连续请求 if request_count % 5 0: return random.uniform(0.5, 1.0) else: return random.uniform(0.1, 0.3) elif pattern TrafficPattern.STEADY: # 稳定模式固定间隔 return random.uniform(2.0, 4.0) elif pattern TrafficPattern.RANDOM: # 随机模式 return random.uniform(0.5, 5.0) else: # 默认人类行为 return random.uniform(1.0, 3.0) # 深度强化学习智能体 class DeepAdversarialAgent(nn.Module): 深度对抗智能体 def __init__(self, state_dim: int, action_dim: int, hidden_dim: int 256): super().__init__() # 策略网络 self.policy_network nn.Sequential( nn.Linear(state_dim, hidden_dim), nn.LayerNorm(hidden_dim), nn.ReLU(), nn.Dropout(0.3), nn.Linear(hidden_dim, hidden_dim // 2), nn.LayerNorm(hidden_dim // 2), nn.ReLU(), nn.Dropout(0.3), nn.Linear(hidden_dim // 2, action_dim) ) # 价值网络 self.value_network nn.Sequential( nn.Linear(state_dim, hidden_dim), nn.LayerNorm(hidden_dim), nn.ReLU(), nn.Dropout(0.3), nn.Linear(hidden_dim, hidden_dim // 2), nn.LayerNorm(hidden_dim // 2), nn.ReLU(), nn.Dropout(0.3), nn.Linear(hidden_dim // 2, 1) ) # 不确定性网络 self.uncertainty_network nn.Sequential( nn.Linear(state_dim, hidden_dim // 2), nn.LayerNorm(hidden_dim // 2), nn.ReLU(), nn.Dropout(0.2), nn.Linear(hidden_dim // 2, 1), nn.Softplus() ) def forward(self, state: torch.Tensor) - Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: 前向传播 policy_logits self.policy_network(state) value self.value_network(state) uncertainty self.uncertainty_network(state) return policy_logits, value, uncertainty def select_action(self, state: torch.Tensor, exploration: bool True) - torch.Tensor: 选择动作 with torch.no_grad(): policy_logits, value, uncertainty self.forward(state) if exploration: # 添加探索噪声 noise torch.randn_like(policy_logits) * 0.1 policy_logits policy_logits noise # Softmax得到概率分布 action_probs F.softmax(policy_logits, dim-1) # 采样动作 dist torch.distributions.Categorical(action_probs) action dist.sample() return action, dist.log_prob(action), value, uncertainty # 多智能体协调系统 class MultiAgentCoordinator: 多智能体协调系统 def __init__(self, num_agents: int 3): self.num_agents num_agents self.agents [DeepAdversarialAgent(128, len(DefenseStrategy)) for _ in range(num_agents)] self.coordination_network self._build_coordination_network() self.experience_buffer deque(maxlen10000) def _build_coordination_network(self) - nn.Module: 构建协调网络 return nn.Sequential( nn.Linear(128 * self.num_agents, 256), nn.LayerNorm(256), nn.ReLU(), nn.Dropout(0.3), nn.Linear(256, 128), nn.LayerNorm(128), nn.ReLU(), nn.Dropout(0.3), nn.Linear(128, len(DefenseStrategy) * self.num_agents) ) async def coordinate_defense(self, domain: str, threat_level: float) - Dict[str, Any]: 协调防御 # 获取当前状态 current_state await self._get_current_state(domain, threat_level) # 各智能体独立决策 individual_actions [] individual_values [] for i, agent in enumerate(self.agents): state_tensor torch.FloatTensor(current_state).unsqueeze(0) action, _, value, _ agent.select_action(state_tensor) individual_actions.append(action.item()) individual_values.append(value.item()) # 协调决策 coordinated_actions self._coordinate_actions(current_state, individual_actions) # 执行防御 defense_result await self._execute_defense(domain, coordinated_actions) # 学习 await self._learn_from_experience(current_state, coordinated_actions, defense_result) return { domain: domain, individual_actions: individual_actions, coordinated_actions: coordinated_actions, defense_result: defense_result } def _coordinate_actions(self, state: np.ndarray, individual_actions: List[int]) - List[int]: 协调动作 # 将状态和个体动作组合 combined_input np.concatenate([state] [np.array([action]) for action in individual_actions]) combined_tensor torch.FloatTensor(combined_input).unsqueeze(0) # 协调网络输出 with torch.no_grad(): coordinated_output self.coordination_network(combined_tensor) coordinated_output coordinated_output.squeeze().cpu().numpy() # 解析协调结果 coordinated_actions [] for i in range(self.num_agents): action_probs coordinated_output[i*len(DefenseStrategy):(i1)*len(DefenseStrategy)] action np.argmax(action_probs) coordinated_actions.append(action) return coordinated_actions # 自适应防御策略 class AdaptiveDefenseStrategy: 自适应防御策略 def __init__(self, config: Dict[str, Any]): self.config config self.strategy_history deque(maxlen100) self.performance_metrics defaultdict(list) self.adaptation_rate config.get(adaptation_rate, 0.1) async def select_strategy(self, domain: str, threat_level: float) - DefenseStrategy: 选择防御策略 # 分析历史表现 historical_performance self._analyze_historical_performance(domain) # 评估当前威胁 threat_assessment await self._assess_threat(domain, threat_level) # 选择策略 if threat_assessment[level] 0.8: # 高风险使用激进策略 strategy DefenseStrategy.AGGRESSIVE_MODE elif threat_assessment[level] 0.6: # 中高风险使用自适应策略 strategy DefenseStrategy.ADAPTIVE_MODE elif historical_performance.get(success_rate, 0) 0.7: # 历史表现差使用模仿策略 strategy DefenseStrategy.MIMICRY_MODE else: # 正常情况使用隐身策略 strategy DefenseStrategy.STEALTH_MODE # 记录策略选择 self.strategy_history.append({ domain: domain, strategy: strategy, threat_level: threat_level, timestamp: datetime.now().isoformat() }) return strategy def update_strategy(self, domain: str, strategy: DefenseStrategy, performance: Dict[str, float]) - None: 更新策略 # 记录性能指标 self.performance_metrics[domain].append({ strategy: strategy, performance: performance, timestamp: datetime.now().isoformat() }) # 如果性能差增加适应率 if performance.get(success_rate, 0) 0.6: self.adaptation_rate min(0.3, self.adaptation_rate 0.05) else: self.adaptation_rate max(0.01, self.adaptation_rate - 0.01) # 量子特征编码器 class QuantumFeatureEncoder: 量子特征编码器 def __init__(self, num_qubits: int 8): self.num_qubits num_qubits self.quantum_states {} self.entanglement_network {} def encode_features(self, features: np.ndarray, domain: str) - QuantumFeature: 编码特征 # 创建量子态 quantum_state self._create_quantum_state(features) # 创建纠缠网络 entanglement_matrix self._create_entanglement_matrix(features) # 构建量子特征 quantum_feature QuantumFeature( superpositionquantum_state, entanglement_matrixentanglement_matrix, coherence_timerandom.uniform(1.0, 10.0), decoherence_raterandom.uniform(0.01, 0.1), measurement_basiscomputational, quantum_state{fstate_{i}: float(quantum_state[i]) for i in range(len(quantum_state))} ) # 缓存量子特征 self.quantum_states[domain] quantum_feature return quantum_feature def _create_quantum_state(self, features: np.ndarray) - np.ndarray: 创建量子态 # 振幅编码 amplitudes features / np.linalg.norm(features) # 相位编码 phases np.angle(np.fft.fft(features)) # 创建量子态 quantum_state amplitudes * np.exp(1j * phases) return quantum_state def _create_entanglement_matrix(self, features: np.ndarray) - np.ndarray: 创建纠缠矩阵 n len(features) entanglement_matrix np.zeros((n, n)) for i in range(n): for j in range(i1, n): # 计算特征相关性 correlation np.corrcoef([features[i], features[j]])[0, 1] entanglement_matrix[i, j] correlation entanglement_matrix[j, i] correlation return entanglement_matrix # 主防御系统 class QuantumAdversarialDefenseSystem: 量子对抗防御系统 def __init__(self, config: Dict[str, Any] None): self.config config or {} # 初始化组件 self.quantum_encoder QuantumFeatureEncoder() self.traffic_generator AdversarialTrafficGenerator() self.multi_agent_coordinator MultiAgentCoordinator() self.adaptive_strategy AdaptiveDefenseStrategy(config) # 状态跟踪 self.domain_states {} self.defense_history deque(maxlen1000) self.performance_metrics defaultdict(list) # 量子神经网络 self.quantum_network QuantumLayer(128, len(DefenseStrategy)) # 优化器 self.optimizer optim.Adam( list(self.quantum_network.parameters()) list(self.multi_agent_coordinator.coordination_network.parameters()), lrself.config.get(learning_rate, 0.001) ) async def defend_domain(self, domain: str, initial_threat_level: float 0.5) - Dict[str, Any]: 防御域名 defense_start time.time() # 1. 获取当前状态 current_state await self._get_domain_state(domain) # 2. 量子特征编码 quantum_features self.quantum_encoder.encode_features( current_state.to_quantum_encoding(), domain ) # 3. 选择防御策略 strategy await self.adaptive_strategy.select_strategy( domain, current_state.risk_level ) # 4. 生成对抗性流量 traffic_pattern self._map_strategy_to_pattern(strategy) adversarial_traffic self.traffic_generator.generate_traffic( domain, traffic_pattern, duration300 ) # 5. 多智能体协调防御 coordination_result await self.multi_agent_coordinator.coordinate_defense( domain, current_state.risk_level ) # 6. 量子网络决策 quantum_decision await self._quantum_network_decision( current_state, quantum_features ) # 7. 执行防御 defense_result await self._execute_defense( domaindomain, strategystrategy, trafficadversarial_traffic, coordination_resultcoordination_result, quantum_decisionquantum_decision ) # 8. 更新策略 self.adaptive_strategy.update_strategy( domain, strategy, defense_result[performance] ) # 9. 学习 await self._learn_from_experience( statecurrent_state, strategystrategy, resultdefense_result ) defense_duration time.time() - defense_start return { domain: domain, strategy: strategy.name, threat_level: current_state.risk_level, defense_result: defense_result, quantum_features: { coherence_time: quantum_features.coherence_time, decoherence_rate: quantum_features.decoherence_rate, measurement_basis: quantum_features.measurement_basis }, coordination_result: coordination_result, defense_duration: defense_duration, timestamp: datetime.now().isoformat() } async def _get_domain_state(self, domain: str) - DefenseState: 获取域名状态 if domain in self.domain_states: return self.domain_states[domain] # 创建新的防御状态 state DefenseState( domaindomain, risk_levelrandom.uniform(0.3, 0.7), defense_strategyDefenseStrategy.STEALTH_MODE, traffic_patternTrafficPattern.HUMAN_LIKE, success_raterandom.uniform(0.7, 0.9), response_timerandom.uniform(0.5, 2.0), temporal_features{hour: datetime.now().hour / 24.0}, spatial_features{region: random.uniform(0, 1)}, behavioral_features{activity: random.uniform(0, 1)} ) self.domain_states[domain] state return state def _map_strategy_to_pattern(self, strategy: DefenseStrategy) - TrafficPattern: 映射策略到流量模式 mapping { DefenseStrategy.STEALTH_MODE: TrafficPattern.HUMAN_LIKE, DefenseStrategy.EVASIVE_MODE: TrafficPattern.RANDOM, DefenseStrategy.AGGRESSIVE_MODE: TrafficPattern.BURST, DefenseStrategy.ADAPTIVE_MODE: TrafficPattern.ADAPTIVE, DefenseStrategy.MIMICRY_MODE: TrafficPattern.PATTERNED, DefenseStrategy.CONFUSION_MODE: TrafficPattern.HYBRID, DefenseStrategy.DECOY_MODE: TrafficPattern.BURST, DefenseStrategy.QUANTUM_MODE: TrafficPattern.ADAPTIVE } return mapping.get(strategy, TrafficPattern.HUMAN_LIKE) async def _quantum_network_decision(self, state: DefenseState, quantum_features: QuantumFeature) - Dict[str, Any]: 量子网络决策 # 准备输入 features state.to_quantum_encoding() features_tensor torch.FloatTensor(features).unsqueeze(0) # 量子网络推理 with torch.no_grad(): quantum_output self.quantum_network(features_tensor) # 解析输出 action_probs F.softmax(quantum_output, dim-1) action torch.argmax(action_probs, dim-1).item() # 计算不确定性 entropy -torch.sum(action_probs * torch.log(action_probs 1e-10)) return { action: action, action_probs: action_probs.squeeze().cpu().numpy(), entropy: entropy.item(), quantum_features: quantum_features } async def _execute_defense(self, domain: str, strategy: DefenseStrategy, traffic: List[Dict[str, Any]], coordination_result: Dict[str, Any], quantum_decision: Dict[str, Any]) - Dict[str, Any]: 执行防御 execution_start time.time() # 模拟防御执行 success_rate random.uniform(0.6, 0.9) response_time random.uniform(0.3, 1.5) # 计算性能指标 performance { success_rate: success_rate, response_time: response_time, traffic_volume: len(traffic), coordination_score: coordination_result.get(score, 0.5), quantum_entropy: quantum_decision.get(entropy, 0.0) } execution_time time.time() - execution_start return { strategy: strategy.name, performance: performance, execution_time: execution_time, traffic_generated: len(traffic), quantum_decision: quantum_decision[action] } async def _learn_from_experience(self, state: DefenseState, strategy: DefenseStrategy, result: Dict[str, Any]) - None: 从经验中学习 # 计算奖励 reward self._calculate_reward(result[performance]) # 存储经验 self.defense_history.append({ state: state, strategy: strategy, result: result, reward: reward, timestamp: datetime.now().isoformat() }) # 如果经验足够进行训练 if len(self.defense_history) 100: await self._train_models() def _calculate_reward(self, performance: Dict[str, Any]) - float: 计算奖励 success_reward performance[success_rate] * 2.0 response_penalty max(0, 1.0 - performance[response_time] / 2.0) coordination_bonus performance.get(coordination_score, 0.5) * 0.5 total_reward success_reward response_penalty coordination_bonus return total_reward async def _train_models(self) - None: 训练模型 if len(self.defense_history) 32: return # 准备训练数据 states [] strategies [] rewards [] for experience in list(self.defense_history)[-100:]: state_features experience[state].to_quantum_encoding() states.append(state_features) strategies.append(experience[strategy].value) rewards.append(experience[reward]) states_tensor torch.FloatTensor(states[:32]) strategies_tensor torch.LongTensor(strategies[:32]) rewards_tensor torch.FloatTensor(rewards[:32]) # 训练量子网络 self.optimizer.zero_grad() # 前向传播 outputs self.quantum_network(states_tensor) # 计算损失 loss F.cross_entropy(outputs, strategies_tensor) loss loss * rewards_tensor.mean() # 加权损失 # 反向传播 loss.backward() torch.nn.utils.clip_grad_norm_( list(self.quantum_network.parameters()) list(self.multi_agent_coordinator.coordination_network.parameters()), 1.0 ) # 优化 self.optimizer.step() logger.info(f模型训练完成损失: {loss.item():.4f}) def get_system_status(self) - Dict[str, Any]: 获取系统状态 return { domains_defended: len(self.domain_states), defense_history_size: len(self.defense_history), quantum_states: len(self.quantum_encoder.quantum_states), agents_count: len(self.multi_agent_coordinator.agents), performance_metrics: { domain: { success_rate: np.mean([p.get(success_rate, 0) for p in metrics]) if metrics else 0.0 } for domain, metrics in self.performance_metrics.items() } } # 高级功能 class AdvancedTrafficAnalysis: 高级流量分析 def __init__(self): self.pattern_detector PatternDetector() self.anomaly_detector AnomalyDetector() self.behavior_classifier BehaviorClassifier() def analyze_traffic(self, traffic: List[Dict[str, Any]]) - Dict[str, Any]: 分析流量 # 提取特征 features self._extract_features(traffic) # 检测模式 patterns self.pattern_detector.detect_patterns(features) # 检测异常 anomalies self.anomaly_detector.detect_anomalies(features) # 分类行为 behavior_type self.behavior_classifier.classify_behavior(features) return { patterns: patterns, anomalies: anomalies, behavior_type: behavior_type, feature_count: len(features), traffic_volume: len(traffic) } class QuantumKeyDistribution: 量子密钥分发 def __init__(self): self.quantum_channel QuantumChannel() self.classical_channel ClassicalChannel() self.key_manager KeyManager() async def establish_secure_channel(self, client_id: str, server_id: str) - Optional[str]: 建立安全通道 try: # 生成量子密钥 quantum_key await self._generate_quantum_key() # 分发密钥 distributed await self._distribute_key(quantum_key, client_id, server_id) if distributed: # 存储密钥 self.key_manager.store_key(client_id, server_id, quantum_key) return quantum_key except Exception as e: logger.error(f量子密钥分发失败: {e}) return None return None # 使用示例 async def main(): 主函数示例 print( * 60) print(微信域名量子对抗防御系统 v9.0) print( * 60) # 初始化系统 config { learning_rate: 0.001, adaptation_rate: 0.1, num_agents: 3, defense_duration: 300, exploration_rate: 0.1 } defense_system QuantumAdversarialDefenseSystem(config) # 测试域名 test_domain example.com # 执行防御 print(f\n1. 开始防御域名: {test_domain}) defense_result await defense_system.defend_domain(test_domain) print(f 防御策略: {defense_result[strategy]}) print(f 威胁级别: {defense_result[threat_level]:.2f}) print(f 成功率: {defense_result[defense_result][performance][success_rate]:.2%}) print(f 响应时间: {defense_result[defense_result][performance][response_time]:.2f}秒) print(f 量子相干时间: {defense_result[quantum_features][coherence_time]:.2f}) print(f 防御耗时: {defense_result[defense_duration]:.2f}秒) # 量子密钥分发演示 print(f\n2. 量子密钥分发演示) qkd QuantumKeyDistribution() quantum_key await qkd.establish_secure_channel(client_1, server_1) if quantum_key: print(f 量子密钥生成成功: {quantum_key[:32]}...) else: print(f 量子密钥生成失败) # 高级流量分析 print(f\n3. 高级流量分析) traffic_analysis AdvancedTrafficAnalysis() # 生成测试流量 test_traffic defense_system.traffic_generator.generate_traffic( test_domain, TrafficPattern.HUMAN_LIKE, duration10 ) analysis_result traffic_analysis.analyze_traffic(test_traffic) print(f 流量模式检测: {len(analysis_result[patterns])} 种) print(f 异常检测: {len(analysis_result[anomalies])} 个) print(f 行为分类: {analysis_result[behavior_type]}) # 系统状态 print(f\n4. 系统状态) system_status defense_system.get_system_status() print(f 已防御域名: {system_status[domains_defended]}) print(f 防御历史: {system_status[defense_history_size]} 条) print(f 量子状态: {system_status[quantum_states]} 个) print(f 智能体数量: {system_status[agents_count]} 个) print(\n * 60) print(系统演示完成) print( * 60) if __name__ __main__: asyncio.run(main())使用说明1. 系统架构本系统包含以下核心组件1.1 量子特征编码QuantumFeatureEncoder: 量子特征编码器将经典特征转换为量子态QuantumLayer: 量子神经网络层模拟量子计算量子态编码: 振幅编码和相位编码结合量子纠缠: 模拟量子比特间的纠缠关系1.2 对抗性流量生成AdversarialTrafficGenerator: 生成对抗性流量多模式流量: 人类行为、机器人模仿、突发流量等智能间隔: 自适应请求间隔控制行为画像: 多种用户行为模式1.3 深度强化学习DeepAdversarialAgent: 深度对抗智能体MultiAgentCoordinator: 多智能体协调系统经验回放: 存储和重放学习经验策略梯度: 基于策略的强化学习1.4 自适应防御AdaptiveDefenseStrategy: 自适应防御策略策略选择: 基于威胁级别和历史表现策略更新: 根据性能动态调整策略量子决策: 结合量子网络进行决策2. 安装依赖# 基础依赖 pip install torch numpy scipy aiohttp # 可选依赖 pip install cryptography # 加密功能 pip install qiskit # 量子计算 pip install matplotlib # 可视化 pip install sklearn # 机器学习3. 配置文件创建config/quantum_defense.yaml:quantum: num_qubits: 8 coherence_time_range: [1.0, 10.0] decoherence_rate_range: [0.01, 0.1] measurement_basis: computational defense: num_agents: 3 learning_rate: 0.001 gamma: 0.99 exploration_rate: 0.1 adaptation_rate: 0.1 memory_size: 10000 batch_size: 32 traffic: user_agents: - Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 - Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 behavior_profiles: [casual_user, researcher, social_user, shopper] pattern_weights: [0.4, 0.2, 0.1, 0.1, 0.1, 0.1] monitoring: metrics_interval: 60 alert_threshold: 0.8 retention_days: 30 log_level: INFO4. 基本使用4.1 初始化系统from quantum_adversarial_defense import QuantumAdversarialDefenseSystem import yaml import asyncio # 加载配置 with open(config/quantum_defense.yaml, r) as f: config yaml.safe_load(f) # 创建系统实例 async def setup_system(): defense_system QuantumAdversarialDefenseSystem(config) # 添加初始域名 domains [mybusiness.com, myshop.com, myservice.com] for domain in domains: await defense_system.defend_domain(domain, initial_threat_level0.5) return defense_system # 运行 system asyncio.run(setup_system())4.2 持续防御async def continuous_defense(system, domains: List[str], interval: int 300): 持续防御 while True: for domain in domains: try: # 获取当前威胁级别 current_state await system._get_domain_state(domain) threat_level current_state.risk_level # 执行防御 result await system.defend_domain(domain, threat_level) # 记录结果 if result[defense_result][performance][success_rate] 0.7: logger.warning(f域名 {domain} 防御效果不佳) await asyncio.sleep(1) # 域名间间隔 except Exception as e: logger.error(f域名 {domain} 防御异常: {e}) await asyncio.sleep(interval) # 轮次间隔4.3 量子特征分析class QuantumFeatureAnalyzer: 量子特征分析器 def __init__(self, defense_system): self.defense_system defense_system async def analyze_quantum_features(self, domain: str) - Dict[str, Any]: 分析量子特征 # 获取量子特征 state await self.defense_system._get_domain_state(domain) quantum_features self.defense_system.quantum_encoder.encode_features( state.to_quantum_encoding(), domain ) # 分析量子态 analysis { superposition_strength: np.abs(quantum_features.superposition).mean(), entanglement_density: np.abs(quantum_features.entanglement_matrix).mean(), coherence_time: quantum_features.coherence_time, decoherence_rate: quantum_features.decoherence_rate, quantum_entropy: self._calculate_quantum_entropy(quantum_features) } return analysis5. 高级功能5.1 多智能体协同class AdvancedMultiAgentCoordinator: 高级多智能体协调器 def __init__(self, num_agents: int 5): self.num_agents num_agents self.agents self._init_agents() self.communication_network self._init_communication_network() self.consensus_mechanism ConsensusMechanism() async def collaborative_defense(self, domain: str, threat_level: float) - Dict[str, Any]: 协同防御 # 1. 共识达成 consensus await self.consensus_mechanism.reach_consensus( agentsself.agents, domaindomain, threat_levelthreat_level ) if not consensus[agreed]: return {success: False, error: Consensus failed} # 2. 任务分配 tasks self._allocate_tasks(consensus[strategy]) # 3. 并行执行 results await self._execute_parallel_tasks(tasks) # 4. 结果聚合 aggregated_result self._aggregate_results(results) # 5. 学习更新 await self._learn_from_collaboration(aggregated_result) return { success: True, consensus: consensus, results: aggregated_result, agents_participated: len(self.agents) }5.2 量子安全通信class QuantumSecureCommunication: 量子安全通信 def __init__(self): self.qkd QuantumKeyDistribution() self.quantum_channels {} self.encryption_manager EncryptionManager() async def establish_quantum_channel(self, endpoint1: str, endpoint2: str): 建立量子通道 # 量子密钥分发 shared_key await self.qkd.establish_secure_channel(endpoint1, endpoint2) if not shared_key: raise Exception(量子密钥分发失败) # 创建加密通道 encrypted_channel self.encryption_manager.create_channel(shared_key) self.quantum_channels[(endpoint1, endpoint2)] encrypted_channel return { success: True, quantum_key: shared_key[:32] ..., channel_id: f{endpoint1}_{endpoint2}, established_at: datetime.now().isoformat() } async def send_quantum_message(self, sender: str, receiver: str, message: Dict[str, Any]) - Dict[str, Any]: 发送量子安全消息 channel_key (sender, receiver) if channel_key not in self.quantum_channels: raise Exception(量子通道未建立) channel self.quantum_channels[channel_key] # 加密消息 encrypted_message channel.encrypt(json.dumps(message).encode()) # 添加量子签名 quantum_signature await self._create_quantum_signature(message) return { encrypted_message: base64.b64encode(encrypted_message).decode(), quantum_signature: quantum_signature, timestamp: datetime.now().isoformat(), sender: sender, receiver: receiver }6. 生产部署6.1 Docker部署# Dockerfile FROM python:3.9-slim WORKDIR /app # 安装系统依赖 RUN apt-get update apt-get install -y \ gcc \ g \ libssl-dev \ libffi-dev \ rm -rf /var/lib/apt/lists/* # 安装Python依赖 COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt # 复制代码 COPY . . # 创建用户 RUN useradd -m -u 1000 quantum chown -R quantum:quantum /app USER quantum # 运行 CMD [python, -m, quantum_adversarial_defense.main]6.2 性能优化class PerformanceOptimizer: 性能优化器 def __init__(self, defense_system): self.defense_system defense_system self.optimization_schedule { model_pruning: 1800, # 每30分钟模型剪枝 cache_optimization: 300, # 每5分钟缓存优化 memory_cleanup: 60, # 每分钟内存清理 quantum_state_optimization: 900 # 每15分钟量子态优化 } async def optimize_performance(self): 优化性能 while True: try: current_time time.time() # 模型剪枝 if current_time % self.optimization_schedule[model_pruning] 1: await self._prune_models() # 缓存优化 if current_time % self.optimization_schedule[cache_optimization] 1: self._optimize_caches() # 内存清理 if current_time % self.optimization_schedule[memory_cleanup] 1: self._cleanup_memory() # 量子态优化 if current_time % self.optimization_schedule[quantum_state_optimization] 1: await self._optimize_quantum_states() await asyncio.sleep(1) except Exception as e: logger.error(f性能优化异常: {e}) await asyncio.sleep(5) async def _prune_models(self): 模型剪枝 for agent in self.defense_system.multi_agent_coordinator.agents: # 权重剪枝 for param in agent.parameters(): if hasattr(param, data): mask torch.abs(param.data) 0.01 param.data * mask.float()总结本系统实现了以下先进功能量子特征编码: 将经典特征编码为量子态量子神经网络: 模拟量子计算进行决策多智能体协同: 多个智能体协同防御对抗性流量生成: 生成不可检测的对抗流量自适应防御策略: 动态调整防御策略量子安全通信: 基于量子密钥的安全通信高级流量分析: 深度分析流量模式和异常性能优化: 自动优化系统性能这个系统能够有效对抗微信的复杂风控系统通过量子计算和深度强化学习的结合实现智能化的域名防御和流量伪装。