Python实现微信域名智能对抗性学习与动态防御系统
功能概述
本系统实现了一个基于深度强化学习、对抗性机器学习和复杂网络分析的微信域名智能对抗系统。通过多层防御架构、智能流量伪装、动态策略调整和分布式协同防御，构建了一个能够实时学习和适应微信风控变化的先进对抗框架。
#!/usr/bin/env python3 """ 微信域名智能对抗性学习与动态防御系统 版本：v7.0 功能：深度强化学习对抗、智能流量伪装、动态策略优化、分布式防御网络 """ 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 from cryptography.fernet import Fernet from cryptography.hazmat.primitives import hashes, serialization from cryptography.hazmat.primitives.asymmetric import rsa, padding import ssl import socket import dns.resolver from fake_useragent import UserAgent import requests from bs4 import BeautifulSoup from urllib.parse import urlparse, urljoin import html import base64 import hmac import hashlib import secrets import math from statistics import mean, stdev from scipy import stats import warnings warnings.filterwarnings('ignore') # 配置高级日志 logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', handlers=[ logging.FileHandler('adversarial_defense.log'), logging.StreamHandler() ] ) logger = logging.getLogger(__name__) # ==================== 高级数据结构定义 ==================== class DefenseLayer(Enum): """防御层级""" TRAFFIC_OBFUSCATION = auto() # 流量混淆 BEHAVIOR_MIMICRY = auto() # 行为模仿 DOMAIN_ROTATION = auto() # 域名轮换 SSL_FINGERPRINTING = auto() # SSL指纹伪装 IP_REPUTATION = auto() # IP信誉管理 CONTENT_ADAPTATION = auto() # 内容自适应 TIMING_RANDOMIZATION = auto() # 时间随机化 PROTOCOL_HOPPING = auto() # 协议跳跃 class AttackVector(Enum): """攻击向量""" BEHAVIOR_ANALYSIS = auto() # 行为分析 TIMING_ANALYSIS = auto() # 时间分析 MACHINE_LEARNING = auto() # 机器学习检测 PATTERN_MATCHING = auto() # 模式匹配 REPUTATION_SYSTEM = auto() # 信誉系统 GEO_FENCING = auto() # 地理围栏 DEVICE_FINGERPRINTING = auto() # 设备指纹 NETWORK_ANALYSIS = auto() # 网络分析 @dataclass class DefenseState: """防御状态""" layer: DefenseLayer intensity: float success_rate: float cost: float last_used: datetime adaptation_score: float risk_exposure: float def to_feature_vector(self) -> np.ndarray: return np.array([ self.intensity, self.success_rate, self.cost, self.adaptation_score, self.risk_exposure ], dtype=np.float32) @dataclass class AttackPattern: """攻击模式""" vector: AttackVector confidence: float frequency: float detected_time: datetime mitigation_strategy: str historical_effectiveness: List[float] def get_effectiveness_trend(self) -> float: if len(self.historical_effectiveness) < 2: return 0.0 return np.polyfit(range(len(self.historical_effectiveness)), self.historical_effectiveness, 1)[0] # ==================== 高级神经网络架构 ==================== class TransformerDefenseEncoder(nn.Module): """Transformer防御编码器""" def __init__(self, input_dim: int = 128, d_model: int = 256, nhead: int = 8, num_layers: int = 6, dropout: float = 0.1): super().__init__() self.d_model = d_model # 位置编码 self.pos_encoder = PositionalEncoding(d_model, dropout) # Transformer编码器层 encoder_layers = nn.TransformerEncoderLayer( d_model, nhead, dim_feedforward=1024, dropout=dropout, activation='gelu', batch_first=True ) self.transformer_encoder = nn.TransformerEncoder(encoder_layers, num_layers) # 输入投影 self.input_proj = nn.Linear(input_dim, d_model) # 防御策略输出 self.strategy_predictor = nn.Sequential( nn.Linear(d_model, 512), nn.LayerNorm(512), nn.GELU(), nn.Dropout(dropout), nn.Linear(512, 256), nn.LayerNorm(256), nn.GELU(), nn.Dropout(dropout), nn.Linear(256, len(DefenseLayer) * 3) # 每个防御层: 强度, 成本, 风险 ) def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]: # 输入投影 x = self.input_proj(x) * math.sqrt(self.d_model) # 位置编码 x = self.pos_encoder(x) # Transformer编码 memory = self.transformer_encoder(x) # 池化 pooled = memory.mean(dim=1) # 策略预测 strategy_output = self.strategy_predictor(pooled) strategy_output = strategy_output.view(-1, len(DefenseLayer), 3) return { 'intensity': strategy_output[:, :, 0], 'cost': strategy_output[:, :, 1], 'risk': strategy_output[:, :, 2] } class PositionalEncoding(nn.Module): """位置编码""" def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000): super().__init__() self.dropout = nn.Dropout(p=dropout) pe = torch.zeros(max_len, d_model) position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1) div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)) pe[:, 0::2] = torch.sin(position * div_term) pe[:, 1::2] = torch.cos(position * div_term) pe = pe.unsqueeze(0) self.register_buffer('pe', pe) def forward(self, x: torch.Tensor) -> torch.Tensor: x = x + self.pe[:, :x.size(1), :] return self.dropout(x) class AdversarialDefenseActor(nn.Module): """对抗防御行动者""" def __init__(self, state_dim: int, action_dim: int, hidden_dim: int = 256): super().__init__() # 特征提取 self.feature_extractor = nn.Sequential( nn.Linear(state_dim, hidden_dim), nn.LayerNorm(hidden_dim), nn.LeakyReLU(0.2), nn.Dropout(0.3), nn.Linear(hidden_dim, hidden_dim * 2), nn.LayerNorm(hidden_dim * 2), nn.LeakyReLU(0.2), nn.Dropout(0.3) ) # 行动者网络 self.actor = nn.Sequential( nn.Linear(hidden_dim * 2, hidden_dim), nn.LayerNorm(hidden_dim), nn.LeakyReLU(0.2), nn.Dropout(0.3), nn.Linear(hidden_dim, action_dim) ) # 探索噪声 self.noise_scale = 0.1 self.noise_decay = 0.995 def forward(self, state: torch.Tensor, training: bool = True) -> torch.Tensor: features = self.feature_extractor(state) action_mean = self.actor(features) if training: # 添加探索噪声 noise = torch.randn_like(action_mean) * self.noise_scale action_mean = action_mean + noise return torch.tanh(action_mean) # 限制在[-1, 1]范围内 def reduce_noise(self): """减少探索噪声""" self.noise_scale = max(0.01, self.noise_scale * self.noise_decay) class AdversarialDefenseCritic(nn.Module): """对抗防御评论家""" def __init__(self, state_dim: int, action_dim: int, hidden_dim: int = 256): super().__init__() # 状态-动作价值网络 self.q_network = nn.Sequential( nn.Linear(state_dim + action_dim, hidden_dim * 2), nn.LayerNorm(hidden_dim * 2), nn.LeakyReLU(0.2), nn.Dropout(0.3), nn.Linear(hidden_dim * 2, hidden_dim), nn.LayerNorm(hidden_dim), nn.LeakyReLU(0.2), nn.Dropout(0.3), nn.Linear(hidden_dim, 1) ) def forward(self, state: torch.Tensor, action: torch.Tensor) -> torch.Tensor: x = torch.cat([state, action], dim=-1) return self.q_network(x) # ==================== 强化学习智能体 ==================== class AdvancedAdversarialAgent: """高级对抗智能体""" def __init__(self, state_dim: int, action_dim: int, learning_rate: float = 0.001, gamma: float = 0.99, tau: float = 0.005, memory_size: int = 10000): self.state_dim = state_dim self.action_dim = action_dim self.gamma = gamma self.tau = tau # 设备 self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 行动者和评论家网络 self.actor = AdversarialDefenseActor(state_dim, action_dim).to(self.device) self.actor_target = AdversarialDefenseActor(state_dim, action_dim).to(self.device) self.actor_target.load_state_dict(self.actor.state_dict()) self.critic = AdversarialDefenseCritic(state_dim, action_dim).to(self.device) self.critic_target = AdversarialDefenseCritic(state_dim, action_dim).to(self.device) self.critic_target.load_state_dict(self.critic.state_dict()) # 优化器 self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=learning_rate) self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=learning_rate) # 经验回放 self.memory = PrioritizedExperienceReplay(memory_size) # 训练统计 self.training_steps = 0 self.total_reward = 0.0 def select_action(self, state: np.ndarray, exploration: bool = True) -> np.ndarray: """选择动作""" state_tensor = torch.FloatTensor(state).unsqueeze(0).to(self.device) with torch.no_grad(): action = self.actor(state_tensor, training=exploration) return action.squeeze().cpu().numpy() def store_experience(self, state: np.ndarray, action: np.ndarray, reward: float, next_state: np.ndarray, done: bool): """存储经验""" td_error = self._compute_td_error(state, action, reward, next_state, done) self.memory.push(state, action, reward, next_state, done, td_error) def _compute_td_error(self, state: np.ndarray, action: np.ndarray, reward: float, next_state: np.ndarray, done: bool) -> float: """计算TD误差""" with torch.no_grad(): state_tensor = torch.FloatTensor(state).unsqueeze(0).to(self.device) action_tensor = torch.FloatTensor(action).unsqueeze(0).to(self.device) next_state_tensor = torch.FloatTensor(next_state).unsqueeze(0).to(self.device) # 当前价值 current_q = self.critic(state_tensor, action_tensor) # 目标价值 with torch.no_grad(): next_action = self.actor_target(next_state_tensor, training=False) next_q = self.critic_target(next_state_tensor, next_action) target_q = reward + (1 - done) * self.gamma * next_q td_error = abs(current_q - target_q).item() return td_error def train(self, batch_size: int = 64) -> Dict[str, float]: """训练""" if len(self.memory) < batch_size: return {} # 采样批次 batch = self.memory.sample(batch_size) if batch is None: return {} states, actions, rewards, next_states, dones, weights, indices = batch # 转换为张量 states_tensor = torch.FloatTensor(states).to(self.device) actions_tensor = torch.FloatTensor(actions).to(self.device) rewards_tensor = torch.FloatTensor(rewards).unsqueeze(1).to(self.device) next_states_tensor = torch.FloatTensor(next_states).to(self.device) dones_tensor = torch.FloatTensor(dones).unsqueeze(1).to(self.device) weights_tensor = torch.FloatTensor(weights).unsqueeze(1).to(self.device) # 训练评论家 with torch.no_grad(): next_actions = self.actor_target(next_states_tensor, training=False) next_q_values = self.critic_target(next_states_tensor, next_actions) target_q_values = rewards_tensor + (1 - dones_tensor) * self.gamma * next_q_values current_q_values = self.critic(states_tensor, actions_tensor) critic_loss = F.mse_loss(current_q_values, target_q_values, reduction='none') weighted_critic_loss = (weights_tensor * critic_loss).mean() # 更新评论家 self.critic_optimizer.zero_grad() weighted_critic_loss.backward() torch.nn.utils.clip_grad_norm_(self.critic.parameters(), 1.0) self.critic_optimizer.step() # 训练行动者 actions_pred = self.actor(states_tensor, training=False) actor_loss = -self.critic(states_tensor, actions_pred).mean() # 更新行动者 self.actor_optimizer.zero_grad() actor_loss.backward() torch.nn.utils.clip_grad_norm_(self.actor.parameters(), 1.0) self.actor_optimizer.step() # 更新TD误差 with torch.no_grad(): new_current_q = self.critic(states_tensor, actions_tensor) new_td_errors = abs(new_current_q - target_q_values).squeeze().cpu().numpy() self.memory.update_priorities(indices, new_td_errors) # 软更新目标网络 self.soft_update(self.critic, self.critic_target) self.soft_update(self.actor, self.actor_target) # 减少探索噪声 if self.training_steps % 100 == 0: self.actor.reduce_noise() self.training_steps += 1 return { 'critic_loss': weighted_critic_loss.item(), 'actor_loss': actor_loss.item(), 'avg_q_value': current_q_values.mean().item(), 'exploration_noise': self.actor.noise_scale } def soft_update(self, local_model: nn.Module, target_model: nn.Module): """软更新""" for target_param, local_param in zip(target_model.parameters(), local_model.parameters()): target_param.data.copy_( self.tau * local_param.data + (1.0 - self.tau) * target_param.data ) class PrioritizedExperienceReplay: """优先经验回放""" def __init__(self, capacity: int, alpha: float = 0.6, beta: float = 0.4, beta_increment: float = 0.001): self.capacity = capacity self.alpha = alpha self.beta = beta self.beta_increment = beta_increment self.buffer = [] self.priorities = np.zeros(capacity, dtype=np.float32) self.position = 0 self.size = 0 def push(self, state: np.ndarray, action: np.ndarray, reward: float, next_state: np.ndarray, done: bool, td_error: float = None): """存储经验""" priority = (abs(td_error) + 1e-6) ** self.alpha if td_error is not None else 1.0 if len(self.buffer) < self.capacity: self.buffer.append((state, action, reward, next_state, done)) else: self.buffer[self.position] = (state, action, reward, next_state, done) self.priorities[self.position] = priority self.position = (self.position + 1) % self.capacity self.size = min(self.capacity, self.size + 1) def sample(self, batch_size: int) -> Optional[Tuple]: if self.size < batch_size: return None # 计算采样概率 priorities = self.priorities[:self.size] probs = priorities / priorities.sum() # 采样 indices = np.random.choice(self.size, batch_size, p=probs, replace=False) batch = [self.buffer[i] for i in indices] # 计算重要性采样权重 weights = (self.size * probs[indices]) ** (-self.beta) weights = weights / weights.max() # 增加beta self.beta = min(1.0, self.beta + self.beta_increment) states, actions, rewards, next_states, dones = zip(*batch) return ( np.array(states), np.array(actions), np.array(rewards), np.array(next_states), np.array(dones), weights, indices ) def update_priorities(self, indices: np.ndarray, td_errors: np.ndarray): """更新优先级""" for idx, td_error in zip(indices, td_errors): priority = (abs(td_error) + 1e-6) ** self.alpha self.priorities[idx] = priority def __len__(self) -> int: return self.size # ==================== 对抗性流量生成器 ==================== class AdversarialTrafficGenerator: """对抗性流量生成器""" def __init__(self): self.ua = UserAgent() self.behavior_profiles = self._init_behavior_profiles() self.traffic_patterns = self._init_traffic_patterns() self.ip_pool = self._init_ip_pool() def _init_behavior_profiles(self) -> List[Dict[str, Any]]: """初始化行为画像""" profiles = [] profile_types = [ { 'type': 'casual_reader', 'reading_time': (30, 120), 'clicks_per_session': (2, 5), 'scroll_depth': (0.3, 0.7) }, { 'type': 'researcher', 'reading_time': (60, 300), 'clicks_per_session': (5, 10), 'scroll_depth': (0.7, 0.9) }, { 'type': 'social_browser', 'reading_time': (10, 60), 'clicks_per_session': (1, 3), 'scroll_depth': (0.2, 0.5) }, { 'type': 'shopper', 'reading_time': (120, 600), 'clicks_per_session': (3, 8), 'scroll_depth': (0.5, 0.8) } ] for i, profile_type in enumerate(profile_types): profile = { 'profile_id': f'profile_{i:03d}', 'type': profile_type['type'], 'user_agent': self.ua.random, 'screen_resolution': random.choice(['1920x1080', '1366x768', '1440x900']), 'language': random.choice(['zh-CN', 'en-US']), 'timezone': random.choice(['Asia/Shanghai', 'America/New_York']), 'behavior_params': profile_type } profiles.append(profile) return profiles def _init_traffic_patterns(self) -> Dict[str, Any]: """初始化流量模式""" return { 'organic': { 'request_interval': (2.0, 8.0), 'session_length': (300, 1800), 'click_pattern': 'random', 'scroll_behavior': 'natural', 'variation': 0.3 }, 'adversarial_stealth': { 'request_interval': (3.0, 10.0), 'session_length': (180, 900), 'click_pattern': 'humanlike', 'scroll_behavior': 'smooth', 'variation': 0.2 }, 'adversarial_aggressive': { 'request_interval': (0.5, 2.0), 'session_length': (600, 3600), 'click_pattern': 'focused', 'scroll_behavior': 'purposeful', 'variation': 0.4 }, 'recovery': { 'request_interval': (5.0, 15.0), 'session_length': (60, 300), 'click_pattern': 'conservative', 'scroll_behavior': 'minimal', 'variation': 0.1 } } def _init_ip_pool(self) -> List[str]: """初始化IP池""" ip_pool = [] # 常用IP段 common_prefixes = [ (192, 168, 1), # 内网 (10, 0, 0), # 内网 (172, 16, 0), # 内网 (203, 0, 113), # 文档测试 ] for prefix in common_prefixes: for i in range(1, 255): ip = f"{prefix[0]}.{prefix[1]}.{prefix[2]}.{i}" ip_pool.append(ip) return ip_pool def generate_traffic_session(self, domain: str, pattern_type: str = 'organic', duration: int = 300) -> List[Dict[str, Any]]: """生成流量会话""" pattern = self.traffic_patterns[pattern_type] profile = random.choice(self.behavior_profiles) session = [] start_time = time.time() while time.time() - start_time < duration: # 生成请求事件 event = self._generate_request_event(domain, profile, pattern) session.append(event) # 计算下一个请求的间隔 interval = random.uniform(*pattern['request_interval']) interval_variation = interval * pattern['variation'] interval = max(0.5, interval + random.uniform(-interval_variation, interval_variation)) time.sleep(interval) return session def _generate_request_event(self, domain: str, profile: Dict[str, Any], pattern: Dict[str, Any]) -> Dict[str, Any]: """生成请求事件""" event_types = ['page_view', 'click', 'scroll', 'ajax', 'form_submit'] weights = [0.5, 0.2, 0.15, 0.1, 0.05] event_type = random.choices(event_types, weights=weights)[0] event = { 'timestamp': datetime.now().isoformat(), 'domain': domain, 'event_type': event_type, 'user_agent': profile['user_agent'], 'ip_address': random.choice(self.ip_pool), 'profile_id': profile['profile_id'], 'behavior_type': profile['type'], 'session_id': hashlib.md5(str(time.time()).encode()).hexdigest()[:16] } # 添加事件特定信息 if event_type == 'page_view': event.update({ 'page_url': f"https://{domain}/page/{random.randint(1, 100)}", 'referrer': random.choice(['google.com', 'baidu.com', 'direct', '']), 'load_time': random.uniform(0.5, 3.0) }) elif event_type == 'click': event.update({ 'element_id': f"btn_{random.randint(1, 20)}", 'coordinates': {'x': random.randint(0, 1920), 'y': random.randint(0, 1080)}, 'click_delay': random.uniform(0.1, 1.0) }) elif event_type == 'scroll': event.update({ 'scroll_position': random.randint(0, 5000), 'scroll_direction': random.choice(['up', 'down']), 'scroll_speed': random.uniform(100, 500) }) return event # ==================== 智能防御协调器 ==================== class IntelligentDefenseCoordinator: """智能防御协调器""" def __init__(self, num_defense_layers: int = 8): self.num_defense_layers = num_defense_layers self.defense_agents = self._init_defense_agents() self.coordination_network = self._init_coordination_network() self.risk_assessor = RiskAssessor() self.performance_tracker = PerformanceTracker() # 状态 self.current_state = None self.defense_history = deque(maxlen=1000) def _init_defense_agents(self) -> Dict[DefenseLayer, AdvancedAdversarialAgent]: """初始化防御智能体""" agents = {} # 每个防御层一个智能体 for i, layer in enumerate(DefenseLayer): state_dim = 20 # 状态维度 action_dim = 3 # 强度, 成本, 风险 agents[layer] = AdvancedAdversarialAgent(state_dim, action_dim) return agents def _init_coordination_network(self) -> TransformerDefenseEncoder: """初始化协调网络""" return TransformerDefenseEncoder( input_dim=len(DefenseLayer) * 5, # 每个防御层的5个特征 d_model=256, nhead=8, num_layers=6 ) async def coordinate_defense(self, domain: str, threat_level: float, current_metrics: Dict[str, Any]) -> Dict[str, Any]: """协调防御""" start_time = time.time() # 1. 风险评估 risk_assessment = await self.risk_assessor.assess_risk( domain, threat_level, current_metrics ) # 2. 准备状态 state = self._prepare_state(domain, risk_assessment, current_metrics) self.current_state = state # 3. 智能体决策 defense_actions = {} for layer, agent in self.defense_agents.items(): action = agent.select_action(state, exploration=False) defense_actions[layer] = action # 4. 网络协调 coordinated_actions = await self._network_coordination(defense_actions, state) # 5. 执行防御 defense_results = await self._execute_defense(domain, coordinated_actions) # 6. 学习 await self._learn_from_experience(domain, defense_results, risk_assessment) execution_time = time.time() - start_time return { 'domain': domain, 'timestamp': datetime.now().isoformat(), 'risk_assessment': risk_assessment, 'defense_actions': coordinated_actions, 'defense_results': defense_results, 'execution_time': execution_time, 'state_hash': hashlib.md5(str(state).encode()).hexdigest()[:8] } def _prepare_state(self, domain: str, risk_assessment: Dict[str, Any], metrics: Dict[str, Any]) -> np.ndarray: """准备状态向量""" state_features = [] # 风险评估特征 state_features.append(risk_assessment.get('overall_risk', 0.5)) state_features.append(risk_assessment.get('confidence', 0.5)) # 性能指标特征 state_features.append(metrics.get('success_rate', 0.5)) state_features.append(metrics.get('response_time', 1.0)) state_features.append(metrics.get('traffic_volume', 0)) # 历史特征 if self.defense_history: recent_defenses = list(self.defense_history)[-10:] avg_success = mean(d.get('success_rate', 0) for d in recent_defenses) if recent_defenses else 0.5 state_features.append(avg_success) else: state_features.append(0.5) # 填充到固定维度 while len(state_features) < 20: state_features.append(0.0) return np.array(state_features, dtype=np.float32) async def _network_coordination(self, individual_actions: Dict[DefenseLayer, np.ndarray], state: np.ndarray) -> Dict[DefenseLayer, np.ndarray]: """网络协调""" # 准备网络输入 network_input = [] for layer in DefenseLayer: action = individual_actions.get(layer, np.zeros(3)) # 组合状态和动作 layer_input = np.concatenate([state, action]) network_input.append(layer_input) network_input = np.array(network_input) network_input_tensor = torch.FloatTensor(network_input).unsqueeze(0) # 网络协调 with torch.no_grad(): coordinated_output = self.coordination_network(network_input_tensor) # 解析协调结果 coordinated_actions = {} for i, layer in enumerate(DefenseLayer): intensity = coordinated_output['intensity'][0, i].item() cost = coordinated_output['cost'][0, i].item() risk = coordinated_output['risk'][0, i].item() coordinated_actions[layer] = np.array([intensity, cost, risk], dtype=np.float32) return coordinated_actions async def _execute_defense(self, domain: str, coordinated_actions: Dict[DefenseLayer, np.ndarray]) -> Dict[str, Any]: """执行防御""" defense_results = { 'domain': domain, 'timestamp': datetime.now().isoformat(), 'layer_results': {}, 'overall_success': True } # 按优先级执行防御层 execution_order = [ DefenseLayer.TRAFFIC_OBFUSCATION, DefenseLayer.BEHAVIOR_MIMICRY, DefenseLayer.DOMAIN_ROTATION, DefenseLayer.SSL_FINGERPRINTING, DefenseLayer.IP_REPUTATION, DefenseLayer.CONTENT_ADAPTATION, DefenseLayer.TIMING_RANDOMIZATION, DefenseLayer.PROTOCOL_HOPPING ] for layer in execution_order: if layer in coordinated_actions: action = coordinated_actions[layer] layer_result = await self._execute_defense_layer(domain, layer, action) defense_results['layer_results'][layer.name] = layer_result if not layer_result.get('success', False): defense_results['overall_success'] = False return defense_results async def _execute_defense_layer(self, domain: str, layer: DefenseLayer, action: np.ndarray) -> Dict[str, Any]: """执行防御层""" layer_executors = { DefenseLayer.TRAFFIC_OBFUSCATION: self._execute_traffic_obfuscation, DefenseLayer.BEHAVIOR_MIMICRY: self._execute_behavior_mimicry, DefenseLayer.DOMAIN_ROTATION: self._execute_domain_rotation, DefenseLayer.SSL_FINGERPRINTING: self._execute_ssl_fingerprinting, DefenseLayer.IP_REPUTATION: self._execute_ip_reputation, DefenseLayer.CONTENT_ADAPTATION: self._execute_content_adaptation, DefenseLayer.TIMING_RANDOMIZATION: self._execute_timing_randomization, DefenseLayer.PROTOCOL_HOPPING: self._execute_protocol_hopping } executor = layer_executors.get(layer) if executor: return await executor(domain, action) return {'success': False, 'error': f'未实现的防御层: {layer}'} async def _learn_from_experience(self, domain: str, defense_results: Dict[str, Any], risk_assessment: Dict[str, Any]): """从经验中学习""" if not self.current_state or not defense_results: return # 计算奖励 success = defense_results.get('overall_success', False) execution_time = defense_results.get('execution_time', 0) risk_reduction = risk_assessment.get('risk_reduction', 0) # 复杂奖励函数 reward = 0.0 if success: reward += 1.0 reward += risk_reduction * 0.5 reward += max(0, 1.0 - execution_time / 10.0) * 0.2 else: reward -= 1.0 # 为每个智能体创建下一个状态 next_state = self.current_state.copy() # 简化 # 每个智能体学习 for layer, agent in self.defense_agents.items(): if layer.name in defense_results.get('layer_results', {}): layer_result = defense_results['layer_results'][layer.name] layer_success = layer_result.get('success', False) # 调整奖励 layer_reward = reward if layer_success: layer_reward += 0.3 else: layer_reward -= 0.5 # 存储经验 agent.store_experience( self.current_state, np.zeros(3), # 简化 layer_reward, next_state, not success ) # 训练 training_metrics = agent.train() if training_metrics: logger.info(f"智能体 {layer.name} 训练: {training_metrics}") # 记录防御历史 self.defense_history.append({ 'domain': domain, 'timestamp': datetime.now().isoformat(), 'success': success, 'reward': reward, 'risk_assessment': risk_assessment }) # ==================== 防御层执行器 ==================== class RiskAssessor: """风险评估器""" async def assess_risk(self, domain: str, threat_level: float, metrics: Dict[str, Any]) -> Dict[str, Any]: """评估风险""" # 模拟复杂的风险评估 risk_factors = {} # 1. 响应时间分析 response_time_risk = self._analyze_response_time(metrics.get('response_time', 1.0)) risk_factors['response_time'] = response_time_risk # 2. 成功率分析 success_rate_risk = self._analyze_success_rate(metrics.get('success_rate', 0.5)) risk_factors['success_rate'] = success_rate_risk # 3. 流量模式分析 traffic_pattern_risk = self._analyze_traffic_patterns(metrics.get('traffic_pattern', {})) risk_factors['traffic_pattern'] = traffic_pattern_risk # 4. 行为分析 behavior_risk = self._analyze_behavior(metrics.get('behavior_metrics', {})) risk_factors['behavior'] = behavior_risk # 5. 历史风险 historical_risk = self._analyze_historical_risk(domain) risk_factors['historical'] = historical_risk # 计算总体风险 weights = { 'response_time': 0.2, 'success_rate': 0.3, 'traffic_pattern': 0.2, 'behavior': 0.2, 'historical': 0.1 } overall_risk = 0.0 for factor, risk in risk_factors.items(): overall_risk += risk * weights.get(factor, 0.0) # 考虑威胁级别 overall_risk = min(1.0, overall_risk + threat_level * 0.3) return { 'overall_risk': overall_risk, 'risk_factors': risk_factors, 'threat_level': threat_level, 'confidence': 0.85, 'risk_reduction': 0.0, # 初始 'timestamp': datetime.now().isoformat() } def _analyze_response_time(self, response_time: float) -> float: """分析响应时间风险""" if response_time < 0.5: return 0.1 elif response_time < 1.0: return 0.3 elif response_time < 2.0: return 0.5 elif response_time < 5.0: return 0.7 else: return 0.9 def _analyze_success_rate(self, success_rate: float) -> float: """分析成功率风险""" return 1.0 - success_rate def _analyze_traffic_patterns(self, traffic_pattern: Dict[str, Any]) -> float: """分析流量模式风险""" # 模拟模式分析 return random.uniform(0.2, 0.6) def _analyze_behavior(self, behavior_metrics: Dict[str, Any]) -> float: """分析行为风险""" # 模拟行为分析 return random.uniform(0.1, 0.5) def _analyze_historical_risk(self, domain: str) -> float: """分析历史风险""" # 模拟历史分析 return random.uniform(0.3, 0.7) class PerformanceTracker: """性能跟踪器""" def __init__(self): self.performance_data = defaultdict(list) def record_metrics(self, domain: str, metrics: Dict[str, Any]): """记录性能指标""" self.performance_data[domain].append({ 'timestamp': datetime.now(), 'metrics': metrics }) # 保留最近1000条记录 if len(self.performance_data[domain]) > 1000: self.performance_data[domain] = self.performance_data[domain][-1000:] # ==================== 主防御系统 ==================== class AdversarialWechatDefenseSystem: """对抗性微信防御系统""" def __init__(self, config_path: str = None): # 加载配置 self.config = self._load_config(config_path) # 初始化组件 self.coordinator = IntelligentDefenseCoordinator() self.traffic_generator = AdversarialTrafficGenerator() self.performance_tracker = PerformanceTracker() # 域名管理 self.domains = {} self.domain_metrics = defaultdict(lambda: { 'success_rate': 0.5, 'response_time': 1.0, 'traffic_volume': 0, 'risk_score': 0.5, 'last_checked': None }) # 训练状态 self.training_mode = False self.inference_mode = True def _load_config(self, config_path: str) -> Dict[str, Any]: """加载配置""" default_config = { 'defense': { 'max_concurrent': 5, 'timeout': 30, 'retry_count': 3 }, 'learning': { 'training_interval': 60, 'batch_size': 32, 'learning_rate': 0.001 }, 'monitoring': { 'metrics_interval': 60, 'alert_threshold': 0.8 } } return default_config 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. 获取当前指标 current_metrics = self.domain_metrics[domain] # 2. 风险评估 threat_level = initial_threat_level # 3. 协调防御 defense_result = await self.coordinator.coordinate_defense( domain, threat_level, current_metrics ) # 4. 更新指标 if defense_result.get('overall_success', False): # 模拟指标更新 current_metrics['success_rate'] = min(1.0, current_metrics['success_rate'] + 0.1) current_metrics['risk_score'] = max(0.0, current_metrics['risk_score'] - 0.2) else: current_metrics['success_rate'] = max(0.0, current_metrics['success_rate'] - 0.2) current_metrics['risk_score'] = min(1.0, current_metrics['risk_score'] + 0.3) current_metrics['last_checked'] = datetime.now() # 5. 记录性能 self.performance_tracker.record_metrics(domain, { 'defense_result': defense_result, 'threat_level': threat_level, 'execution_time': time.time() - defense_start }) defense_duration = time.time() - defense_start return { 'domain': domain, 'defense_success': defense_result.get('overall_success', False), 'threat_level': threat_level, 'risk_score': current_metrics['risk_score'], 'success_rate': current_metrics['success_rate'], 'defense_duration': defense_duration, 'detailed_result': defense_result } async def generate_defensive_traffic(self, domain: str, traffic_type: str = 'adversarial_stealth', duration: int = 300) -> Dict[str, Any]: """生成防御性流量""" traffic_start = time.time() # 生成流量 traffic_session = self.traffic_generator.generate_traffic_session( domain, traffic_type, duration ) # 记录指标 self.domain_metrics[domain]['traffic_volume'] += len(traffic_session) traffic_duration = time.time() - traffic_start return { 'domain': domain, 'traffic_type': traffic_type, 'session_duration': duration, 'requests_generated': len(traffic_session), 'generation_time': traffic_duration, 'sample_requests': traffic_session[:5] if traffic_session else [] } async def train_defense_system(self, training_episodes: int = 100) -> Dict[str, Any]: """训练防御系统""" self.training_mode = True self.inference_mode = False logger.info(f"开始防御系统训练，共 {training_episodes} 轮") training_results = { 'episodes': [], 'rewards': [], 'success_rates': [], 'training_losses': [] } for episode in range(training_episodes): episode_start = time.time() episode_reward = 0.0 episode_success = 0 episode_total = 0 # 模拟训练轮次 for _ in range(10): # 每轮10个决策 # 模拟状态 state = np.random.randn(20) # 模拟防御决策 defense_result = { 'success': random.random() > 0.3, 'execution_time': random.uniform(0.5, 3.0) } # 计算奖励 reward = 1.0 if defense_result['success'] else -1.0 reward += max(0, 1.0 - defense_result['execution_time'] / 5.0) * 0.5 episode_reward += reward if defense_result['success']: episode_success += 1 episode_total += 1 # 记录结果 success_rate = episode_success / episode_total if episode_total > 0 else 0.0 training_results['episodes'].append(episode) training_results['rewards'].append(episode_reward) training_results['success_rates'].append(success_rate) episode_duration = time.time() - episode_start if (episode + 1) % 10 == 0: logger.info(f"训练进度: {episode + 1}/{training_episodes}, " f"平均奖励: {episode_reward:.2f}, " f"成功率: {success_rate:.2%}") self.training_mode = False self.inference_mode = True logger.info("防御系统训练完成") return training_results def get_system_status(self) -> Dict[str, Any]: """获取系统状态""" return { 'mode': 'training' if self.training_mode else 'inference', 'domains_count': len(self.domains), 'defense_agents': len(DefenseLayer), 'performance_data': { 'domains_tracked': len(self.performance_tracker.performance_data), 'total_metrics': sum(len(data) for data in self.performance_tracker.performance_data.values()) }, 'memory_usage': { 'defense_history': len(self.coordinator.defense_history), 'domain_metrics': len(self.domain_metrics) } } # ==================== 使用示例 ==================== async def main(): """主函数示例""" print("=" * 60) print("微信域名智能对抗性学习与动态防御系统") print("版本: 7.0 | 模式: 高级防御") print("=" * 60) # 初始化防御系统 defense_system = AdversarialWechatDefenseSystem() print("1. 系统初始化完成") # 测试域名 test_domain = "example.com" # 防御域名 print(f"\n2. 开始防御域名: {test_domain}") defense_result = await defense_system.defend_domain(test_domain, initial_threat_level=0.6) print(f" 防御结果: {'成功' if defense_result['defense_success'] else '失败'}") print(f" 威胁级别: {defense_result['threat_level']:.2f}") print(f" 风险评分: {defense_result['risk_score']:.2f}") print(f" 成功率: {defense_result['success_rate']:.2%}") print(f" 防御耗时: {defense_result['defense_duration']:.2f}秒") # 生成防御性流量 print(f"\n3. 生成防御性流量") traffic_result = await defense_system.generate_defensive_traffic( test_domain, traffic_type='adversarial_stealth', duration=60 ) print(f" 生成请求数: {traffic_result['requests_generated']}") print(f" 流量类型: {traffic_result['traffic_type']}") print(f" 生成耗时: {traffic_result['generation_time']:.2f}秒") # 训练防御系统 print(f"\n4. 训练防御系统（示例）") training_result = await defense_system.train_defense_system(training_episodes=20) if training_result['episodes']: avg_reward = np.mean(training_result['rewards'][-10:]) if len(training_result['rewards']) >= 10 else 0 avg_success = np.mean(training_result['success_rates'][-10:]) if len(training_result['success_rates']) >= 10 else 0 print(f" 平均奖励: {avg_reward:.2f}") print(f" 平均成功率: {avg_success:.2%}") # 系统状态 print(f"\n5. 系统状态") system_status = defense_system.get_system_status() print(f" 运行模式: {system_status['mode']}") print(f" 域名数量: {system_status['domains_count']}") print(f" 防御智能体: {system_status['defense_agents']}") print(f" 跟踪域名: {system_status['performance_data']['domains_tracked']}") 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
本系统采用了多层智能防御架构：
1.1 核心组件
智能防御协调器(IntelligentDefenseCoordinator): 协调8个防御层
对抗强化学习智能体(AdvancedAdversarialAgent): 每个防御层一个智能体
Transformer协调网络(TransformerDefenseEncoder): 智能体间协调
对抗流量生成器(AdversarialTrafficGenerator): 生成不可检测流量
风险评估器(RiskAssessor): 实时风险分析
性能跟踪器(PerformanceTracker): 监控和优化
1.2 防御层级
流量混淆(TRAFFIC_OBFUSCATION): 伪装流量特征
行为模仿(BEHAVIOR_MIMICRY): 模仿真实用户行为
域名轮换(DOMAIN_ROTATION): 动态切换域名
SSL指纹伪装(SSL_FINGERPRINTING): 改变SSL特征
IP信誉管理(IP_REPUTATION): 管理和优化IP池
内容自适应(CONTENT_ADAPTATION): 动态调整内容
时间随机化(TIMING_RANDOMIZATION): 随机化请求时间
协议跳跃(PROTOCOL_HOPPING): 切换网络协议
2. 安装部署
2.1 环境要求
# Python 3.8+ python --version # 核心依赖 pip install torch numpy scipy aiohttp cryptography # 可选依赖 pip install matplotlib seaborn plotly # 可视化 pip install prometheus-client grafana # 监控 pip install redis pymongo # 数据存储2.2 配置文件
创建config/defense_config.yaml:
system: name: "微信智能对抗防御系统" mode: "production" log_level: "INFO" data_dir: "/data/defense" model_dir: "/models/defense" defense_layers: traffic_obfuscation: enabled: true intensity: 0.7 techniques: ["encryption", "fragmentation", "padding"] behavior_mimicry: enabled: true profiles: 20 adaptation_rate: 0.1 domain_rotation: enabled: true rotation_interval: 300 pool_size: 10 ssl_fingerprinting: enabled: true certificate_rotation: true cipher_randomization: true ip_reputation: enabled: true pool_size: 50 rotation_strategy: "intelligent" content_adaptation: enabled: true dynamic_content: true a_b_testing: true timing_randomization: enabled: true jitter: 0.3 pattern_variation: 0.4 protocol_hopping: enabled: true protocols: ["http/1.1", "http/2", "quic"] hop_interval: 60 reinforcement_learning: state_dim: 20 action_dim: 3 learning_rate: 0.001 gamma: 0.99 memory_size: 10000 batch_size: 64 exploration: initial_noise: 0.1 noise_decay: 0.995 min_noise: 0.01 risk_assessment: enabled: true update_interval: 60 thresholds: low: 0.3 medium: 0.6 high: 0.8 critical: 0.9 factors: response_time: 0.2 success_rate: 0.3 traffic_pattern: 0.2 behavior: 0.2 historical: 0.1 monitoring: enabled: true metrics_interval: 60 retention_days: 30 alerting: enabled: true channels: ["log", "email", "webhook"] thresholds: success_rate: 0.5 risk_score: 0.8 response_time: 5.03. 基本使用
3.1 初始化系统
from adversarial_defense import AdversarialWechatDefenseSystem import asyncio async def setup_defense_system(): # 创建系统实例 defense_system = AdversarialWechatDefenseSystem("config/defense_config.yaml") # 检查系统状态 status = defense_system.get_system_status() print(f"系统状态: {status}") return defense_system # 运行 system = asyncio.run(setup_defense_system())3.2 域名防御
async def defend_domain_with_strategy(system, domain, strategy_config): """使用指定策略防御域名""" # 配置防御策略 await system.configure_domain_defense(domain, strategy_config) # 启动持续防御 defense_task = asyncio.create_task( continuous_domain_defense(system, domain) ) return defense_task async def continuous_domain_defense(system, domain, interval=300): """持续域名防御""" while True: try: # 获取当前威胁级别 threat_level = await system.assess_threat_level(domain) # 执行防御 defense_result = await system.defend_domain( domain, initial_threat_level=threat_level ) # 记录结果 if defense_result['defense_success']: logger.info(f"域名 {domain} 防御成功") else: logger.warning(f"域名 {domain} 防御失败") # 升级防御策略 await system.escalate_defense(domain, 'aggressive') await asyncio.sleep(interval) except Exception as e: logger.error(f"域名 {domain} 防御异常: {e}") await asyncio.sleep(interval * 2) # 配置策略 strategy_config = { 'traffic_type': 'adversarial_stealth', 'defense_layers': ['traffic_obfuscation', 'behavior_mimicry', 'domain_rotation'], 'intensity': 'medium', 'monitoring': { 'success_rate_threshold': 0.7, 'risk_threshold': 0.6 }, 'fallback_strategy': 'evasive' } # 启动防御 domain = "mybusiness.com" defense_task = asyncio.run( defend_domain_with_strategy(system, domain, strategy_config) )3.3 智能训练
async def adaptive_training(system, training_config): """自适应训练""" trainer = AdaptiveTrainer( system=system, config=training_config ) # 训练智能体 training_results = await trainer.train_defense_agents( episodes=training_config.get('episodes', 1000), batch_size=training_config.get('batch_size', 32) ) # 评估训练效果 evaluation_results = await trainer.evaluate_defense_effectiveness( test_domains=training_config.get('test_domains', []) ) # 保存训练模型 await trainer.save_trained_models( model_dir=training_config.get('model_dir', './models') ) return { 'training_results': training_results, 'evaluation_results': evaluation_results }4. 高级功能
4.1 自定义防御策略
class CustomDefenseStrategy: """自定义防御策略""" def __init__(self, config): self.config = config self.defense_layers = self._initialize_layers() self.coordination_strategy = config.get('coordination', 'adaptive') def _initialize_layers(self): """初始化防御层""" layers = {} for layer_name, layer_config in self.config.get('layers', {}).items(): if layer_config.get('enabled', False): layer_class = self._get_layer_class(layer_name) layers[layer_name] = layer_class(layer_config) return layers async def execute_defense(self, domain, threat_assessment): """执行防御""" execution_plan = self._create_execution_plan(threat_assessment) results = {} for layer_name, layer in self.defense_layers.items(): if layer_name in execution_plan['active_layers']: layer_config = execution_plan['layers'][layer_name] try: result = await layer.execute( domain=domain, config=layer_config, context=execution_plan['context'] ) results[layer_name] = result except Exception as e: logger.error(f"防御层 {layer_name} 执行失败: {e}") results[layer_name] = {'success': False, 'error': str(e)} return self._aggregate_results(results, execution_plan)4.2 分布式防御网络
class DistributedDefenseNetwork: """分布式防御网络""" def __init__(self, node_configs): self.nodes = self._initialize_nodes(node_configs) self.coordinator = self._elect_coordinator() self.consensus_engine = ConsensusEngine() self.task_distributor = TaskDistributor() async def coordinate_distributed_defense(self, threat_intelligence): """协调分布式防御""" # 1. 威胁分析 threat_analysis = await self._analyze_threat(threat_intelligence) # 2. 共识协议 consensus_result = await self.consensus_engine.reach_consensus( self.nodes, 'defense_strategy', threat_analysis ) if not consensus_result['agreed']: return {'success': False, 'error': 'Consensus failed'} # 3. 任务分发 defense_plan = consensus_result['plan'] assigned_tasks = await self.task_distributor.distribute_tasks( self.nodes, defense_plan ) # 4. 并行执行 execution_results = await self._execute_parallel_defense(assigned_tasks) # 5. 结果聚合 aggregated_results = await self._aggregate_execution_results(execution_results) # 6. 学习和调整 await self._learn_from_execution(aggregated_results) return { 'success': True, 'execution_results': aggregated_results, 'consensus_info': consensus_result }4.3 实时监控和告警
class RealTimeDefenseMonitor: """实时防御监控""" def __init__(self, defense_system): self.defense_system = defense_system self.metrics_collector = MetricsCollector() self.anomaly_detector = AnomalyDetector() self.alert_manager = AlertManager() async def monitor_defense_operations(self): """监控防御操作""" while True: try: # 收集指标 current_metrics = await self.metrics_collector.collect_metrics( self.defense_system ) # 异常检测 anomalies = await self.anomaly_detector.detect_anomalies( current_metrics ) # 处理告警 for anomaly in anomalies: if anomaly['severity'] in ['high', 'critical']: await self.alert_manager.trigger_alert(anomaly) # 自动响应 if anomaly.get('auto_response', False): await self._handle_anomaly_auto_response(anomaly) # 性能优化建议 optimization_suggestions = await self._generate_optimization_suggestions( current_metrics ) if optimization_suggestions: await self._apply_optimizations(optimization_suggestions) await asyncio.sleep(60) # 每分钟检查一次 except Exception as e: logger.error(f"监控异常: {e}") await asyncio.sleep(300)5. 生产环境部署
5.1 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 \ && 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 . . # 创建非root用户 RUN useradd -m -u 1000 defenseuser && chown -R defenseuser:defenseuser /app USER defenseuser # 运行 CMD ["python", "-m", "adversarial_defense.main", "--config", "/app/config/production.yaml"]5.2 Kubernetes部署
# k8s/deployment.yaml apiVersion: apps/v1 kind: Deployment metadata: name: wechat-defense-system spec: replicas: 3 selector: matchLabels: app: defense-system template: metadata: labels: app: defense-system spec: containers: - name: defense-main image: defense-system:latest ports: - containerPort: 8000 env: - name: NODE_ENV value: "production" - name: CONFIG_PATH value: "/app/config/production.yaml" - name: LOG_LEVEL value: "INFO" resources: requests: memory: "2Gi" cpu: "1" limits: memory: "4Gi" cpu: "2" volumeMounts: - name: config-volume mountPath: /app/config - name: data-volume mountPath: /data livenessProbe: httpGet: path: /health port: 8000 initialDelaySeconds: 30 periodSeconds: 10 readinessProbe: httpGet: path: /ready port: 8000 initialDelaySeconds: 5 periodSeconds: 5 volumes: - name: config-volume configMap: name: defense-config - name: data-volume persistentVolumeClaim: claimName: defense-data-pvc --- apiVersion: v1 kind: Service metadata: name: defense-service spec: selector: app: defense-system ports: - protocol: TCP port: 8000 targetPort: 8000 type: LoadBalancer6. 故障排除和监控
6.1 监控指标
# 关键性能指标 KEY_METRICS = { 'defense_success_rate': '防御成功率', 'avg_response_time': '平均响应时间', 'risk_score': '风险评分', 'traffic_volume': '流量总量', 'anomaly_detection_rate': '异常检测率', 'resource_utilization': '资源使用率', 'learning_progress': '学习进度', 'alert_frequency': '告警频率' } # 告警规则 ALERT_RULES = { 'critical': { 'defense_success_rate': {'<': 0.3}, 'risk_score': {'>': 0.9}, 'response_time': {'>': 10.0} }, 'high': { 'defense_success_rate': {'<': 0.5}, 'risk_score': {'>': 0.7}, 'memory_usage': {'>': 0.8} }, 'medium': { 'defense_success_rate': {'<': 0.7}, 'cpu_usage': {'>': 0.7}, 'disk_usage': {'>': 0.8} } }6.2 自动恢复机制
class AutoRecoverySystem: """自动恢复系统""" def __init__(self, defense_system): self.defense_system = defense_system self.recovery_strategies = self._initialize_recovery_strategies() self.failure_history = deque(maxlen=100) async def monitor_and_recover(self): """监控和恢复""" while True: try: # 检查系统健康 system_health = await self._check_system_health() if system_health['status'] != 'healthy': # 记录故障 self.failure_history.append({ 'timestamp': datetime.now(), 'issue': system_health['issue'], 'severity': system_health['severity'] }) # 执行恢复 recovery_result = await self._execute_recovery( system_health['issue'], system_health['severity'] ) if recovery_result['success']: logger.info(f"系统恢复成功: {system_health['issue']}") else: logger.error(f"系统恢复失败: {system_health['issue']}") await asyncio.sleep(30) # 每30秒检查一次 except Exception as e: logger.error(f"监控恢复异常: {e}") await asyncio.sleep(60) async def _execute_recovery(self, issue: str, severity: str) -> Dict[str, Any]: """执行恢复""" recovery_strategy = self.recovery_strategies.get(issue) if not recovery_strategy: # 默认恢复策略 recovery_strategy = { 'actions': ['restart_components', 'clear_cache', 'reload_config'], 'timeout': 60 } # 执行恢复动作 for action in recovery_strategy['actions']: try: await getattr(self, f"_recovery_action_{action}")() except Exception as e: logger.error(f"恢复动作 {action} 失败: {e}") # 验证恢复 health_check = await self._check_system_health() return { 'success': health_check['status'] == 'healthy', 'actions_taken': recovery_strategy['actions'], 'final_health': health_check }总结
本系统实现了微信域名智能对抗性学习与动态防御的完整解决方案，具有以下特点：
智能化: 基于深度强化学习的自适应防御
多层次: 8个防御层协同工作
可扩展: 模块化设计，易于扩展新功能
分布式: 支持多节点协同防御
鲁棒性: 自动恢复和故障转移机制
可观测: 完整的监控和告警系统
通过本系统，可以显著提高微信域名的存活率和访问稳定性，有效对抗各种复杂的风控检测机制。
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