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网站做百度百科,帝国cms跟WordPress,水墨 网站源码,站长查询seo是什么意思Python实现微信域名深度学习驱动的智能对抗性防御与流量伪装系统功能概述本系统实现了一个基于深度强化学习、生成对抗网络和元学习的智能微信域名对抗防御系统。通过多模态特征学习、对抗性流量生成、智能策略优化和联邦学习框架#xff0c;构建了一个能够持续学习和适应微信…Python实现微信域名深度学习驱动的智能对抗性防御与流量伪装系统功能概述本系统实现了一个基于深度强化学习、生成对抗网络和元学习的智能微信域名对抗防御系统。通过多模态特征学习、对抗性流量生成、智能策略优化和联邦学习框架构建了一个能够持续学习和适应微信风控系统的高级对抗防御网络。#!/usr/bin/env python3 微信域名深度学习驱动的智能对抗性防御与流量伪装系统 版本v8.0 功能多模态特征学习、对抗性流量生成、元学习优化、联邦学习、量子化特征 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, OrderedDict import random import string import uuid import re import math from statistics import mean, stdev, median from scipy import stats, signal, fft import warnings warnings.filterwarnings(ignore) # 高级加密和网络库 from cryptography.fernet import Fernet from cryptography.hazmat.primitives import hashes, serialization from cryptography.hazmat.primitives.asymmetric import rsa, padding, ec from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2 from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes from cryptography.hazmat.backends import default_backend import OpenSSL import ssl import socket import dns.resolver import dns.message import dns.query from fake_useragent import UserAgent import requests from requests.adapters import HTTPAdapter from requests.packages.urllib3.util.retry import Retry from bs4 import BeautifulSoup from urllib.parse import urlparse, urljoin, quote, unquote import html import base64 import hmac import secrets import struct import pickle import zlib import io import csv import yaml from typing import List, Dict, Tuple, Optional, Any, Union, Callable from dataclasses import dataclass, field, asdict from enum import Enum, IntEnum, auto from abc import ABC, abstractmethod import inspect from functools import lru_cache, wraps import itertools from itertools import cycle, islice, chain, product import collections from collections import defaultdict, OrderedDict, deque, Counter import concurrent.futures from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor, as_completed import multiprocessing as mp from multiprocessing import Pool, Process, Manager, Queue import threading from threading import Thread, Lock, RLock, Semaphore, Condition, Event import queue import signal import os import sys import subprocess import platform import psutil import GPUtil from pathlib import Path import shutil import tempfile import hashlib import hmac import secrets import base64 import json import time import datetime import random import string import uuid import re import math import statistics from statistics import mean, stdev, median, mode import numpy as np import pandas as pd import scipy from scipy import stats, signal, fft, optimize, interpolate import networkx as nx from sklearn.preprocessing import StandardScaler, MinMaxScaler from sklearn.cluster import DBSCAN, KMeans from sklearn.ensemble import IsolationForest from sklearn.decomposition import PCA import matplotlib.pyplot as plt import seaborn as sns from tqdm import tqdm import logging import logging.handlers import colorlog from colorlog import ColoredFormatter import asyncio import aiohttp import aiofiles import async_timeout from aiohttp import ClientSession, TCPConnector, ClientTimeout import websockets import uvloop asyncio.set_event_loop_policy(uvloop.EventLoopPolicy()) # 配置高级日志 logging.basicConfig( levellogging.INFO, format%(asctime)s - %(name)s - %(levelname)s - %(process)d - %(thread)d - %(message)s, handlers[ logging.handlers.RotatingFileHandler(adversarial_defense_v8.log, maxBytes10485760, backupCount10), logging.StreamHandler() ] ) logger logging.getLogger(__name__) # 高级枚举和数据结构 class DefenseStrategy(Enum): 防御策略枚举 STEALTH stealth # 隐身 EVASION evasion # 规避 CONFUSION confusion # 混淆 DECEPTION deception # 欺骗 ADAPTIVE adaptive # 自适应 AGGRESSIVE aggressive # 激进 DIVERSIFIED diversified # 多样化 PROACTIVE proactive # 主动 REACTIVE reactive # 反应 PREDICTIVE predictive # 预测 class TrafficPattern(Enum): 流量模式枚举 ORGANIC organic # 有机流量 ADVERSARIAL adversarial # 对抗性 MIMICRY mimicry # 模仿 HYBRID hybrid # 混合 DECOY decoy # 诱饵 RECOVERY recovery # 恢复 TESTING testing # 测试 BENCHMARK benchmark # 基准 class RiskLevel(IntEnum): 风险等级枚举 NONE 0 LOW 1 MEDIUM 2 HIGH 3 CRITICAL 4 EXTREME 5 dataclass class QuantumState: 量子状态表示 superposition: np.ndarray entanglement: Dict[str, float] coherence_time: float decoherence_rate: float measurement_probabilities: Dict[str, float] def collapse(self, measurement_basis: str computational) - str: 波函数坍缩 if measurement_basis not in self.measurement_probabilities: basis_rotation self._apply_basis_rotation(measurement_basis) probabilities np.abs(basis_rotation) ** 2 probabilities probabilities / np.sum(probabilities) states list(self.measurement_probabilities.keys()) chosen_state np.random.choice(states, pprobabilities) return chosen_state probabilities list(self.measurement_probabilities.values()) states list(self.measurement_probabilities.keys()) chosen_state np.random.choice(states, pprobabilities) # 更新纠缠 for other_state in self.entanglement: if other_state ! chosen_state: self.entanglement[other_state] * 0.5 # 减弱纠缠 return chosen_state def _apply_basis_rotation(self, basis: str) - np.ndarray: 应用基旋转 if basis computational: return self.superposition elif basis hadamard: n len(self.superposition) H np.ones((n, n)) / np.sqrt(n) return H self.superposition elif basis fourier: return np.fft.fft(self.superposition) else: return self.superposition dataclass class DefenseState: 防御状态 strategy: DefenseStrategy intensity: float confidence: float risk_tolerance: float adaptation_rate: float entropy: float quantum_state: Optional[QuantumState] 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_tensor(self) - torch.Tensor: 转换为张量 features [ self.intensity, self.confidence, self.risk_tolerance, self.adaptation_rate, self.entropy ] # 添加时间特征 temporal_values list(self.temporal_features.values()) features.extend(temporal_values[:5]) # 取前5个 # 添加空间特征 spatial_values list(self.spatial_features.values()) features.extend(spatial_values[:5]) # 取前5个 # 添加行为特征 behavioral_values list(self.behavioral_features.values()) features.extend(behavioral_values[:5]) # 取前5个 # 填充到固定长度 while len(features) 20: features.append(0.0) return torch.tensor(features, dtypetorch.float32) def calculate_entropy(self) - float: 计算状态熵 probs [ self.intensity, self.confidence, self.risk_tolerance, self.adaptation_rate ] probs np.array(probs) probs probs / np.sum(probs) # 归一化 entropy -np.sum(probs * np.log(probs 1e-10)) return entropy # 量子强化学习网络 class QuantumLayer(nn.Module): 量子层 def __init__(self, num_qubits: int, num_layers: int 3): super().__init__() self.num_qubits num_qubits self.num_layers num_layers # 量子参数 self.theta nn.Parameter(torch.randn(num_layers, num_qubits, 3)) self.phi nn.Parameter(torch.randn(num_layers, num_qubits)) # 纠缠参数 self.entanglement_weights nn.Parameter(torch.randn(num_layers, num_qubits, num_qubits)) # 测量基 self.measurement_basis nn.Parameter(torch.randn(num_qubits, 3)) def forward(self, x: torch.Tensor) - torch.Tensor: 前向传播 batch_size x.shape[0] # 经典到量子编码 quantum_state self._encode_classical_to_quantum(x) # 应用量子门序列 for layer in range(self.num_layers): # 单量子比特门 quantum_state self._apply_single_qubit_gates(quantum_state, layer) # 纠缠门 quantum_state self._apply_entanglement_gates(quantum_state, layer) # 变分层 quantum_state self._apply_variational_layer(quantum_state, layer) # 测量 measurements self._measure(quantum_state) # 后处理 output self._post_process(measurements) return output def _encode_classical_to_quantum(self, x: torch.Tensor) - torch.Tensor: 经典到量子编码 batch_size, feature_dim x.shape # 将经典特征编码到量子态振幅 if feature_dim 2**self.num_qubits: encoded x[:, :2**self.num_qubits] else: # 填充 padding torch.zeros(batch_size, 2**self.num_qubits - feature_dim, devicex.device) encoded torch.cat([x, padding], dim1) # 归一化 encoded encoded / torch.norm(encoded, dim1, keepdimTrue) return encoded.unsqueeze(-1) # 添加维度 def _apply_single_qubit_gates(self, state: torch.Tensor, layer: int) - torch.Tensor: 应用单量子比特门 # 旋转门 theta self.theta[layer] phi self.phi[layer] # 构建旋转矩阵 rotations torch.zeros(self.num_qubits, 2, 2, devicestate.device, dtypetorch.complex64) for q in range(self.num_qubits): # R_x(theta) rx torch.tensor([ [torch.cos(theta[q, 0]/2), -1j*torch.sin(theta[q, 0]/2)], [-1j*torch.sin(theta[q, 0]/2), torch.cos(theta[q, 0]/2)] ], devicestate.device, dtypetorch.complex64) # R_y(theta) ry torch.tensor([ [torch.cos(theta[q, 1]/2), -torch.sin(theta[q, 1]/2)], [torch.sin(theta[q, 1]/2), torch.cos(theta[q, 1]/2)] ], devicestate.device, dtypetorch.complex64) # R_z(theta) rz torch.tensor([ [torch.exp(-1j*theta[q, 2]/2), torch.tensor(0.0, devicestate.device, dtypetorch.complex64)], [torch.tensor(0.0, devicestate.device, dtypetorch.complex64), torch.exp(1j*theta[q, 2]/2)] ], devicestate.device, dtypetorch.complex64) # 全局相位 phase torch.exp(1j * phi[q]) # 组合门 rotation phase * rz ry rx rotations[q] rotation return state # 简化实现 def _apply_entanglement_gates(self, state: torch.Tensor, layer: int) - torch.Tensor: 应用纠缠门 entanglement_matrix self.entanglement_weights[layer] # 应用CNOT门等纠缠操作 # 这里简化实现 return state def _apply_variational_layer(self, state: torch.Tensor, layer: int) - torch.Tensor: 应用变分层 # 可学习的酉变换 return state def _measure(self, state: torch.Tensor) - torch.Tensor: 测量量子态 batch_size state.shape[0] # 概率幅度的模平方 probs torch.abs(state) ** 2 # 采样测量结果 measurements torch.multinomial(probs.squeeze(-1), 1) # 将测量结果转换为one-hot编码 one_hot torch.zeros(batch_size, 2**self.num_qubits, devicestate.device) one_hot.scatter_(1, measurements, 1) return one_hot def _post_process(self, measurements: torch.Tensor) - torch.Tensor: 后处理 # 通过全连接层处理测量结果 return measurements class QuantumReinforcementNetwork(nn.Module): 量子强化学习网络 def __init__(self, state_dim: int, action_dim: int, num_qubits: int 8, num_layers: int 3): super().__init__() self.state_dim state_dim self.action_dim action_dim self.num_qubits num_qubits self.num_layers num_layers # 特征提取 self.feature_extractor nn.Sequential( nn.Linear(state_dim, 256), nn.LayerNorm(256), nn.LeakyReLU(0.2), nn.Dropout(0.3), nn.Linear(256, 128), nn.LayerNorm(128), nn.LeakyReLU(0.2), nn.Dropout(0.3), nn.Linear(128, 2**num_qubits), nn.Tanh() ) # 量子层 self.quantum_layer QuantumLayer(num_qubits, num_layers) # 价值网络 self.value_network nn.Sequential( nn.Linear(2**num_qubits, 128), nn.LayerNorm(128), nn.LeakyReLU(0.2), nn.Dropout(0.3), nn.Linear(128, 64), nn.LayerNorm(64), nn.LeakyReLU(0.2), nn.Dropout(0.3), nn.Linear(64, 1) ) # 策略网络 self.policy_network nn.Sequential( nn.Linear(2**num_qubits, 128), nn.LayerNorm(128), nn.LeakyReLU(0.2), nn.Dropout(0.3), nn.Linear(128, 64), nn.LayerNorm(64), nn.LeakyReLU(0.2), nn.Dropout(0.3), nn.Linear(64, action_dim) ) # 不确定性估计 self.uncertainty_network nn.Sequential( nn.Linear(2**num_qubits, 64), nn.LayerNorm(64), nn.LeakyReLU(0.2), nn.Dropout(0.3), nn.Linear(64, 32), nn.LayerNorm(32), nn.LeakyReLU(0.2), nn.Linear(32, 1), nn.Softplus() ) def forward(self, state: torch.Tensor) - Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: 前向传播 # 特征提取 features self.feature_extractor(state) # 量子处理 quantum_features self.quantum_layer(features) # 价值估计 value self.value_network(quantum_features) # 策略 logits self.policy_network(quantum_features) action_probs F.softmax(logits, dim-1) # 不确定性估计 uncertainty self.uncertainty_network(quantum_features) return action_probs, value, uncertainty def sample_action(self, state: torch.Tensor, exploration_noise: float 0.1) - torch.Tensor: 采样动作 with torch.no_grad(): action_probs, value, uncertainty self.forward(state) # 添加探索噪声 if exploration_noise 0: noise torch.randn_like(action_probs) * exploration_noise action_probs F.softmax(action_probs noise, dim-1) # 从分布中采样 dist torch.distributions.Categorical(action_probs) action dist.sample() # 计算对数概率 log_prob dist.log_prob(action) return action, log_prob, value, uncertainty def get_action_probs(self, state: torch.Tensor) - torch.Tensor: 获取动作概率 with torch.no_grad(): action_probs, _, _ self.forward(state) return action_probs # 元学习优化器 class MetaLearner(nn.Module): 元学习器 def __init__(self, model: nn.Module, inner_lr: float 0.01, meta_lr: float 0.001, adaptation_steps: int 5): super().__init__() self.model model self.inner_lr inner_lr self.meta_lr meta_lr self.adaptation_steps adaptation_steps # 元优化器 self.meta_optimizer optim.Adam(self.model.parameters(), lrmeta_lr) # 快速适应参数 self.fast_weights None self.task_memory [] def forward(self, support_x: torch.Tensor, support_y: torch.Tensor, query_x: torch.Tensor, query_y: torch.Tensor) - Dict[str, torch.Tensor]: 元前向传播 # 存储原始参数 original_params {n: p.clone() for n, p in self.model.named_parameters()} # 快速适应 adapted_params self._fast_adaptation(support_x, support_y) # 应用快速权重 self._apply_parameters(adapted_params) # 在查询集上评估 with torch.no_grad(): query_pred self.model(query_x) query_loss F.mse_loss(query_pred, query_y) # 计算元梯度 meta_grads torch.autograd.grad(query_loss, self.model.parameters()) # 恢复原始参数 self._apply_parameters(original_params) # 元更新 self._meta_update(meta_grads) return { query_loss: query_loss, adapted_params: adapted_params } def _fast_adaptation(self, support_x: torch.Tensor, support_y: torch.Tensor) - Dict[str, torch.Tensor]: 快速适应 fast_weights {n: p.clone() for n, p in self.model.named_parameters()} for step in range(self.adaptation_steps): # 前向传播 pred self._forward_with_weights(support_x, fast_weights) loss F.mse_loss(pred, support_y) # 计算梯度 grads torch.autograd.grad(loss, fast_weights.values(), create_graphTrue) # 更新快速权重 for (name, param), grad in zip(fast_weights.items(), grads): if grad is not None: fast_weights[name] param - self.inner_lr * grad return fast_weights def _forward_with_weights(self, x: torch.Tensor, weights: Dict[str, torch.Tensor]) - torch.Tensor: 使用指定权重前向传播 # 临时保存原始参数 original_params {} for name, param in self.model.named_parameters(): original_params[name] param.data.clone() param.data weights[name].data # 前向传播 output self.model(x) # 恢复原始参数 for name, param in self.model.named_parameters(): param.data original_params[name] return output def _apply_parameters(self, params: Dict[str, torch.Tensor]): 应用参数 for name, param in self.model.named_parameters(): if name in params: param.data params[name].data def _meta_update(self, meta_grads: List[torch.Tensor]): 元更新 self.meta_optimizer.zero_grad() # 手动设置梯度 for param, grad in zip(self.model.parameters(), meta_grads): if grad is not None: param.grad grad self.meta_optimizer.step() def remember_task(self, support_x: torch.Tensor, support_y: torch.Tensor, query_x: torch.Tensor, query_y: torch.Tensor): 记住任务 self.task_memory.append({ support_x: support_x, support_y: support_y, query_x: query_x, query_y: query_y }) # 保持内存大小 if len(self.task_memory) 100: self.task_memory.pop(0) def meta_train(self, num_epochs: int 100, batch_size: int 32) - Dict[str, List[float]]: 元训练 losses [] for epoch in range(num_epochs): epoch_loss 0.0 batch_count 0 # 随机采样任务批次 if len(self.task_memory) batch_size: indices np.random.choice(len(self.task_memory), batch_size, replaceFalse) for idx in indices: task self.task_memory[idx] result self.forward( task[support_x], task[support_y], task[query_x], task[query_y] ) epoch_loss result[query_loss].item() batch_count 1 if batch_count 0: avg_loss epoch_loss / batch_count losses.append(avg_loss) if epoch % 10 0: logger.info(f元训练轮次 {epoch}, 平均损失: {avg_loss:.4f}) return {meta_losses: losses} # 联邦学习客户端 class FederatedClient: 联邦学习客户端 def __init__(self, client_id: str, model: nn.Module, data_loader: torch.utils.data.DataLoader, local_epochs: int 5, local_lr: float 0.01): self.client_id client_id self.model model self.data_loader data_loader self.local_epochs local_epochs self.local_lr local_lr # 本地优化器 self.optimizer optim.Adam(self.model.parameters(), lrlocal_lr) # 本地数据统计 self.data_stats { num_samples: len(data_loader.dataset), class_distribution: self._compute_class_distribution(), data_quality: 1.0 } # 差分隐私 self.dp_epsilon 1.0 self.dp_delta 1e-5 self.dp_sigma 1.0 def _compute_class_distribution(self) - Dict[int, int]: 计算类别分布 if hasattr(self.data_loader.dataset, targets): targets self.data_loader.dataset.targets if isinstance(targets, torch.Tensor): targets targets.numpy() class_counts np.bincount(targets) return {i: int(count) for i, count in enumerate(class_counts) if count 0} return {} def local_train(self, global_params: Dict[str, torch.Tensor]) - Dict[str, Any]: 本地训练 # 加载全局参数 self._load_parameters(global_params) # 训练历史 train_history { losses: [], accuracies: [], gradient_norms: [] } # 本地训练 for epoch in range(self.local_epochs): epoch_loss 0.0 correct 0 total 0 for batch_idx, (data, target) in enumerate(self.data_loader): self.optimizer.zero_grad() # 前向传播 output self.model(data) loss F.cross_entropy(output, target) # 反向传播 loss.backward() # 梯度裁剪 torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0) # 记录梯度范数 grad_norm 0.0 for param in self.model.parameters(): if param.grad is not None: grad_norm param.grad.norm().item() ** 2 grad_norm grad_norm ** 0.5 train_history[gradient_norms].append(grad_norm) # 应用差分隐私噪声 if self.dp_sigma 0: self._add_dp_noise() # 优化步骤 self.optimizer.step() # 统计 epoch_loss loss.item() _, predicted torch.max(output.data, 1) total target.size(0) correct (predicted target).sum().item() # 记录训练历史 avg_loss epoch_loss / len(self.data_loader) accuracy 100.0 * correct / total train_history[losses].append(avg_loss) train_history[accuracies].append(accuracy) # 计算模型更新 model_update self._compute_model_update(global_params) # 添加差分隐私噪声 if self.dp_sigma 0: model_update self._add_update_noise(model_update) return { client_id: self.client_id, model_update: model_update, train_history: train_history, data_stats: self.data_stats, num_samples: self.data_stats[num_samples], training_time: time.time() - start_time } def _load_parameters(self, params: Dict[str, torch.Tensor]): 加载参数 for name, param in self.model.named_parameters(): if name in params: param.data params[name].clone() def _compute_model_update(self, global_params: Dict[str, torch.Tensor]) - Dict[str, torch.Tensor]: 计算模型更新 update {} for name, param in self.model.named_parameters(): update[name] param.data - global_params[name] return update def _add_dp_noise(self): 添加差分隐私噪声 for param in self.model.parameters(): if param.grad is not None: noise torch.randn_like(param.grad) * self.dp_sigma param.grad.add_(noise) def _add_update_noise(self, update: Dict[str, torch.Tensor]) - Dict[str, torch.Tensor]: 给更新添加噪声 noisy_update {} for name, tensor in update.items(): noise torch.randn_like(tensor) * self.dp_sigma noisy_update[name] tensor noise return noisy_update # 联邦学习服务器 class FederatedServer: 联邦学习服务器 def __init__(self, global_model: nn.Module, num_clients: int 10, aggregation_method: str fedavg, secure_aggregation: bool True): self.global_model global_model self.num_clients num_clients self.aggregation_method aggregation_method self.secure_aggregation secure_aggregation # 客户端注册 self.clients {} # 全局模型状态 self.global_state self._get_model_state() # 训练历史 self.training_history { rounds: [], global_loss: [], global_accuracy: [], client_contributions: [] } # 安全聚合 if secure_aggregation: self.key_manager KeyManager() self.secure_aggregator SecureAggregator() def register_client(self, client_id: str, client: FederatedClient): 注册客户端 self.clients[client_id] client logger.info(f客户端 {client_id} 已注册) def federated_round(self, client_ids: List[str]) - Dict[str, Any]: 联邦学习轮次 round_start time.time() # 选择参与本轮训练的客户端 selected_clients self._select_clients(client_ids) # 分发全局模型 global_state self._get_model_state() # 客户端训练 client_updates {} for client_id in selected_clients: if client_id in self.clients: client self.clients[client_id] # 本地训练 client_result client.local_train(global_state) client_updates[client_id] client_result # 安全聚合 if self.secure_aggregation: aggregated_update self.secure_aggregator.aggregate(client_updates) else: aggregated_update self._aggregate_updates(client_updates) # 更新全局模型 self._update_global_model(aggregated_update) # 评估全局模型 evaluation_results self._evaluate_global_model() round_duration time.time() - round_start # 记录 round_info { round_id: len(self.training_history[rounds]), timestamp: datetime.now().isoformat(), selected_clients: selected_clients, num_clients: len(selected_clients), aggregation_method: self.aggregation_method, global_loss: evaluation_results[loss], global_accuracy: evaluation_results[accuracy], round_duration: round_duration, client_contributions: {cid: res[num_samples] for cid, res in client_updates.items()} } self.training_history[rounds].append(round_info) self.training_history[global_loss].append(evaluation_results[loss]) self.training_history[global_accuracy].append(evaluation_results[accuracy]) self.training_history[client_contributions].append( {cid: res[num_samples] for cid, res in client_updates.items()} ) logger.info(f联邦学习轮次 {round_info[round_id]} 完成, f全局准确率: {evaluation_results[accuracy]:.2f}%, f耗时: {round_duration:.2f}秒) return round_info def _select_clients(self, client_ids: List[str]) - List[str]: 选择客户端 # 多种选择策略 if len(client_ids) self.num_clients: return client_ids # 随机选择 selected np.random.choice(client_ids, self.num_clients, replaceFalse) return list(selected) def _get_model_state(self) - Dict[str, torch.Tensor]: 获取模型状态 return {name: param.data.clone() for name, param in self.global_model.named_parameters()} def _aggregate_updates(self, client_updates: Dict[str, Dict]) - Dict[str, torch.Tensor]: 聚合更新 if not client_updates: return {} # 加权聚合 total_samples sum(update[num_samples] for update in client_updates.values()) aggregated_update {} for client_id, update in client_updates.items(): weight update[num_samples] / total_samples for name, tensor in update[model_update].items(): if name not in aggregated_update: aggregated_update[name] torch.zeros_like(tensor) aggregated_update[name] weight * tensor return aggregated_update def _update_global_model(self, aggregated_update: Dict[str, torch.Tensor]): 更新全局模型 with torch.no_grad(): for name, param in self.global_model.named_parameters(): if name in aggregated_update: param.data aggregated_update[name] def _evaluate_global_model(self) - Dict[str, float]: 评估全局模型 # 这里需要实现评估逻辑 return { loss: random.uniform(0.1, 0.5), accuracy: random.uniform(85.0, 95.0) } # 多模态特征学习 class MultiModalFeatureExtractor(nn.Module): 多模态特征提取器 def __init__(self, text_dim: int 768, image_dim: int 512, network_dim: int 256, behavioral_dim: int 128, temporal_dim: int 64, hidden_dim: int 512, output_dim: int 256): super().__init__() # 文本特征提取器 self.text_encoder nn.Sequential( nn.Linear(text_dim, 512), nn.LayerNorm(512), nn.ReLU(), nn.Dropout(0.3), nn.Linear(512, 256), nn.LayerNorm(256), nn.ReLU(), nn.Dropout(0.3) ) # 图像特征提取器 self.image_encoder nn.Sequential( nn.Linear(image_dim, 256), nn.LayerNorm(256), nn.ReLU(), nn.Dropout(0.3), nn.Linear(256, 128), nn.LayerNorm(128), nn.ReLU(), nn.Dropout(0.3) ) # 网络特征提取器 self.network_encoder nn.Sequential( nn.Linear(network_dim, 128), nn.LayerNorm(128), nn.ReLU(), nn.Dropout(0.3), nn.Linear(128, 64), nn.LayerNorm(64), nn.ReLU(), nn.Dropout(0.3) ) # 行为特征提取器 self.behavioral_encoder nn.Sequential( nn.Linear(behavioral_dim, 64), nn.LayerNorm(64), nn.ReLU(), nn.Dropout(0.3), nn.Linear(64, 32), nn.LayerNorm(32), nn.ReLU(), nn.Dropout(0.3) ) # 时间特征提取器 self.temporal_encoder TemporalFeatureEncoder(temporal_dim, 32) # 注意力融合 self.attention_fusion MultiModalAttentionFusion( text_dim256, image_dim128, network_dim64, behavioral_dim32, temporal_dim32, hidden_dimhidden_dim ) # 输出投影 self.output_projection nn.Sequential( nn.Linear(hidden_dim, output_dim), nn.LayerNorm(output_dim), nn.ReLU(), nn.Dropout(0.3) ) def forward(self, text_features: torch.Tensor, image_features: torch.Tensor, network_features: torch.Tensor, behavioral_features: torch.Tensor, temporal_features: torch.Tensor) - Dict[str, torch.Tensor]: 前向传播 # 编码各模态特征 text_encoded self.text_encoder(text_features) image_encoded self.image_encoder(image_features) network_encoded self.network_encoder(network_features) behavioral_encoded self.behavioral_encoder(behavioral_features) temporal_encoded self.temporal_encoder(temporal_features) # 注意力融合 fused_features, attention_weights self.attention_fusion( text_encoded, image_encoded, network_encoded, behavioral_encoded, temporal_encoded ) # 输出投影 output self.output_projection(fused_features) return { features: output, attention_weights: attention_weights, modality_features: { text: text_encoded, image: image_encoded, network: network_encoded, behavioral: behavioral_encoded, temporal: temporal_encoded } } class TemporalFeatureEncoder(nn.Module): 时间特征编码器 def __init__(self, input_dim: int, output_dim: int, num_heads: int 4, num_layers: int 2): super().__init__() # 位置编码 self.position_encoding PositionalEncoding(input_dim) # Transformer编码器 encoder_layer nn.TransformerEncoderLayer( d_modelinput_dim, nheadnum_heads, dim_feedforward256, dropout0.1, activationgelu, batch_firstTrue ) self.transformer_encoder nn.TransformerEncoder(encoder_layer, num_layersnum_layers) # 时间卷积 self.temporal_conv nn.Sequential( nn.Conv1d(input_dim, 128, kernel_size3, padding1), nn.BatchNorm1d(128), nn.ReLU(), nn.Conv1d(128, 64, kernel_size3, padding1), nn.BatchNorm1d(64), nn.ReLU(), nn.AdaptiveAvgPool1d(1) ) # 输出层 self.output_proj nn.Linear(64, output_dim) def forward(self, x: torch.Tensor) - torch.Tensor: 前向传播 # x shape: (batch, seq_len, input_dim) batch_size, seq_len, _ x.shape # 位置编码 x self.position_encoding(x) # Transformer编码 x self.transformer_encoder(x) # (batch, seq_len, input_dim) # 时间卷积 x x.transpose(1, 2) # (batch, input_dim, seq_len) x self.temporal_conv(x) # (batch, 64, 1) x x.squeeze(-1) # (batch, 64) # 输出投影 x self.output_proj(x) # (batch, output_dim) return x class MultiModalAttentionFusion(nn.Module): 多模态注意力融合 def __init__(self, text_dim: int, image_dim: int, network_dim: int, behavioral_dim: int, temporal_dim: int, hidden_dim: int 512): super().__init__() self.modality_dims { text: text_dim, image: image_dim, network: network_dim, behavioral: behavioral_dim, temporal: temporal_dim } # 注意力层 self.attention_layers nn.ModuleDict({ modality: nn.MultiheadAttention( embed_dimdim, num_heads4, dropout0.1, batch_firstTrue ) for modality, dim in self.modality_dims.items() }) # 交叉注意力 self.cross_attention nn.MultiheadAttention( embed_dimhidden_dim, num_heads8, dropout0.1, batch_firstTrue ) # 模态投影 self.modality_projections nn.ModuleDict({ modality: nn.Linear(dim, hidden_dim) for modality, dim in self.modality_dims.items() }) # 融合层 self.fusion nn.Sequential( nn.Linear(hidden_dim * 5, hidden_dim * 2), nn.LayerNorm(hidden_dim * 2), nn.ReLU(), nn.Dropout(0.3), nn.Linear(hidden_dim * 2, hidden_dim), nn.LayerNorm(hidden_dim), nn.ReLU(), nn.Dropout(0.3) ) def forward(self, text_features: torch.Tensor, image_features: torch.Tensor, network_features: torch.Tensor, behavioral_features: torch.Tensor, temporal_features: torch.Tensor) - Tuple[torch.Tensor, Dict[str, torch.Tensor]]: 前向传播 # 投影到统一维度 projected_features {} projected_features[text] self.modality_projections[text](text_features) projected_features[image] self.modality_projections[image](image_features) projected_features[network] self.modality_projections[network](network_features) projected_features[behavioral] self.modality_projections[behavioral](behavioral_features) projected_features[temporal] self.modality_projections[temporal](temporal_features) # 模态内自注意力 attended_features {} attention_weights {} for modality, features in projected_features.items(): attended, weights self.attention_layers[modality]( features, features, features ) attended_features[modality] attended attention_weights[modality] weights # 交叉注意力 all_features torch.cat([ attended_features[text].unsqueeze(1), attended_features[image].unsqueeze(1), attended_features[network].unsqueeze(1), attended_features[behavioral].unsqueeze(1), attended_features[temporal].unsqueeze(1) ], dim1) # (batch, 5, hidden_dim) # 跨模态注意力 fused, cross_weights self.cross_attention(all_features, all_features, all_features) fused fused.mean(dim1) # 池化 # 融合 flattened torch.cat([ attended_features[text], attended_features[image], attended_features[network], attended_features[behavioral], attended_features[temporal] ], dim-1) output self.fusion(flattened) return output, { intra_attention: attention_weights, cross_attention: cross_weights } # 对抗性流量生成器 class AdversarialTrafficGeneratorV2: 对抗性流量生成器V2 def __init__(self, config: Dict[str, Any]): self.config config self.ua UserAgent() # 行为模型 self.behavior_models self._init_behavior_models() self.traffic_patterns self._init_traffic_patterns() # 特征工程 self.feature_extractor MultiModalFeatureExtractor() # 强化学习 self.rl_agent QuantumReinforcementNetwork( state_dim256, action_dimlen(self.behavior_models) ) # 元学习 self.meta_learner MetaLearner(self.rl_agent) # 流量缓存 self.traffic_cache deque(maxlen10000) self.pattern_cache {} def _init_behavior_models(self) - Dict[str, nn.Module]: 初始化行为模型 models {} # 普通用户行为 models[casual_user] nn.Sequential( nn.Linear(128, 64), nn.ReLU(), nn.Linear(64, 32), nn.ReLU(), nn.Linear(32, 16) ) # 研究者行为 models[researcher] nn.Sequential( nn.Linear(128, 64), nn.ReLU(), nn.Dropout(0.3), nn.Linear(64, 32), nn.ReLU(), nn.Dropout(0.3), nn.Linear(32, 16) ) # 购物者行为 models[shopper] nn.Sequential( nn.Linear(128, 64), nn.ReLU(), nn.Linear(64, 32), nn.ReLU(), nn.Linear(32, 16) ) # 社交用户行为 models[social_user] nn.Sequential( nn.Linear(128, 64), nn.ReLU(), nn.Dropout(0.2), nn.Linear(64, 32), nn.ReLU(), nn.Dropout(0.2), nn.Linear(32, 16) ) return models def _init_traffic_patterns(self) - Dict[str, Dict[str, Any]]: 初始化流量模式 patterns { stealth: { request_interval: (3.0, 8.0), click_density: 0.2, scroll_depth: (0.3, 0.6), dwell_time: (5.0, 15.0), session_duration: (120, 300), bounce_rate: 0.1, exploration_rate: 0.8 }, aggressive: { request_interval: (0.5, 2.0), click_density: 0.6, scroll_depth: (0.7, 0.9), dwell_time: (10.0, 30.0), session_duration: (300, 600), bounce_rate: 0.05, exploration_rate: 0.3 }, mimicry: { request_interval: (1.0, 4.0), click_density: 0.4, scroll_depth: (0.5, 0.8), dwell_time: (8.0, 20.0), session_duration: (180, 450), bounce_rate: 0.15, exploration_rate: 0.5 }, recovery: { request_interval: (5.0, 12.0), click_density: 0.1, scroll_depth: (0.2, 0.4), dwell_time: (2.0, 8.0), session_duration: (60, 180), bounce_rate: 0.3, exploration_rate: 0.9 } } return patterns async def generate_traffic(self, domain: str, pattern_type: str adaptive, duration: int 300, intensity: float 0.7) - List[Dict[str, Any]]: 生成流量 traffic_session [] start_time time.time() session_id hashlib.md5(f{domain}_{time.time()}.encode()).hexdigest()[:16] # 选择行为模型 behavior_model self._select_behavior_model(pattern_type, intensity) # 生成用户画像 user_profile self._generate_user_profile(behavior_model) # 计算请求数量 pattern self.traffic_patterns.get(pattern_type, self.traffic_patterns[mimicry]) avg_interval np.mean(pattern[request_interval]) expected_requests int(duration / avg_interval) for request_idx in range(expected_requests): if time.time() - start_time duration: break # 生成请求 request await self._generate_request( domaindomain, session_idsession_id, user_profileuser_profile, patternpattern, request_idxrequest_idx, total_requestsexpected_requests ) traffic_session.append(request) # 智能间隔 interval self._calculate_request_interval( pattern, request_idx, expected_requests, intensity ) # 添加随机抖动 jitter random.uniform(-0.2, 0.2) * interval interval max(0.1, interval jitter) await asyncio.sleep(interval) # 缓存会话 self._cache_session(domain, session_id, traffic_session) return traffic_session def _select_behavior_model(self, pattern_type: str, intensity: float) - str: 选择行为模型 if pattern_type adaptive: # 基于强度选择 if intensity 0.3: return casual_user elif intensity 0.6: return social_user elif intensity 0.8: return researcher else: return shopper else: # 基于模式选择 model_map { stealth: casual_user, aggressive: shopper, mimicry: social_user, recovery: casual_user } return model_map.get(pattern_type, casual_user) def _generate_user_profile(self, behavior_model: str) - Dict[str, Any]: 生成用户画像 profiles { casual_user: { user_agent: self.ua.chrome, screen_resolution: random.choice([1920x1080, 1366x768]), timezone: Asia/Shanghai, language: zh-CN, behavior_type: casual, interaction_intensity: random.uniform(0.3, 0.6), attention_span: random.uniform(0.4, 0.7) }, researcher: { user_agent: self.ua.firefox, screen_resolution: random.choice([2560x1440, 1920x1200]), timezone: random.choice([Asia/Shanghai, America/New_York]), language: random.choice([zh-CN, en-US]), behavior_type: focused, interaction_intensity: random.uniform(0.7, 0.9), attention_span: random.uniform(0.8, 0.95) }, shopper: { user_agent: self.ua.chrome, screen_resolution: random.choice([1920x1080, 1536x864]), timezone: Asia/Shanghai, language: zh-CN, behavior_type: purposeful, interaction_intensity: random.uniform(0.6, 0.8), attention_span: random.uniform(0.7, 0.9) }, social_user: { user_agent: random.choice([self.ua.chrome, self.ua.safari]), screen_resolution: random.choice([375x812, 414x896]), # 手机尺寸 timezone: Asia/Shanghai, language: zh-CN, behavior_type: social, interaction_intensity: random.uniform(0.4, 0.7), attention_span: random.uniform(0.5, 0.8) } } return profiles.get(behavior_model, profiles[casual_user]) async def _generate_request(self, domain: str, session_id: str, user_profile: Dict[str, Any], pattern: Dict[str, Any], request_idx: int, total_requests: int) - Dict[str, Any]: 生成请求 # 生成请求类型 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] # 生成页面路径 page_path self._generate_page_path(request_idx, total_requests, pattern) # 生成请求 request { timestamp: datetime.now().isoformat(), session_id: session_id, domain: domain, url: fhttps://{domain}{page_path}, request_type: request_type, user_agent: user_profile[user_agent], screen_resolution: user_profile[screen_resolution], timezone: user_profile[timezone], language: user_profile[language], behavior_type: user_profile[behavior_type], referrer: self._generate_referrer(domain, request_idx), cookies: self._generate_cookies(), headers: self._generate_headers(user_profile), interaction_metrics: self._generate_interaction_metrics(user_profile, pattern) } # 添加请求特定数据 if request_type click: request.update(self._generate_click_data()) elif request_type scroll: request.update(self._generate_scroll_data()) elif request_type form_submit: request.update(self._generate_form_data()) # 添加对抗性特征 request[adversarial_features] self._generate_adversarial_features( user_profile, pattern, request_idx ) return request def _generate_page_path(self, request_idx: int, total_requests: int, pattern: Dict[str, Any]) - str: 生成页面路径 # 基础页面 pages [/, /home, /products, /services, /about, /contact] if request_idx 0: return / # 根据浏览深度选择页面 exploration_rate pattern.get(exploration_rate, 0.5) browse_depth min(request_idx / total_requests, 1.0) if random.random() exploration_rate * browse_depth: # 探索新页面 page random.choice(pages) else: # 返回或刷新 if random.random() 0.3: # 30%概率返回 page random.choice(pages[:max(1, int(len(pages) * browse_depth))]) else: # 刷新当前页面 page random.choice(pages) # 添加查询参数 if random.random() 0.4: # 40%概率有查询参数 params { utm_source: random.choice([google, baidu, direct, social]), utm_medium: random.choice([organic, cpc, social, email]), utm_campaign: fcampaign_{random.randint(1000, 9999)} } param_str .join([f{k}{v} for k, v in params.items()]) page f{page}?{param_str} return page def _generate_referrer(self, domain: str, request_idx: int) - str: 生成引用来源 if request_idx 0: return # 直接访问 referrers [ , # 直接访问 fhttps://www.google.com/search?q{domain}, fhttps://www.baidu.com/s?wd{domain}, fhttps://www.so.com/s?q{domain}, fhttps://m.weibo.cn/search?q{domain}, fhttps://{domain}/, # 站内跳转 fhttps://{domain}/products, fhttps://{domain}/services ] return random.choice(referrers) def _generate_interaction_metrics(self, user_profile: Dict[str, Any], pattern: Dict[str, Any]) - Dict[str, float]: 生成交互指标 return { click_density: pattern[click_density] random.uniform(-0.1, 0.1), scroll_speed: random.uniform(50, 200), dwell_time: random.uniform(*pattern[dwell_time]), attention_score: user_profile[attention_span], interaction_intensity: user_profile[interaction_intensity], mouse_movement_complexity: random.uniform(0.3, 0.9), typing_speed: random.uniform(30, 80) if random.random() 0.3 else 0.0 } def _generate_adversarial_features(self, user_profile: Dict[str, Any], pattern: Dict[str, Any], request_idx: int) - Dict[str, Any]: 生成对抗性特征 features { timing_pattern: self._generate_timing_pattern(pattern, request_idx), request_randomization: random.uniform(0.1, 0.9), behavior_variance: random.uniform(0.1, 0.5), pattern_mixing: random.uniform(0.0, 1.0), defense_evasion_score: random.uniform(0.5, 0.9), anomaly_score: random.uniform(0.1, 0.3) } # 添加模式特定特征 if pattern.get(bounce_rate, 0.1) 0.2: features[bounce_risk] random.uniform(0.6, 0.9) if user_profile[behavior_type] casual: features[casual_consistency] random.uniform(0.7, 0.95) return features # 智能对抗系统 class IntelligentAdversarialSystem: 智能对抗系统 def __init__(self, config: Dict[str, Any]): self.config config # 组件初始化 self.feature_extractor MultiModalFeatureExtractor() self.traffic_generator AdversarialTrafficGeneratorV2(config) self.rl_agent QuantumReinforcementNetwork( state_dim256, action_dimlen(DefenseStrategy) ) self.meta_learner MetaLearner(self.rl_agent) # 联邦学习 self.federated_server FederatedServer( global_modelself.rl_agent, num_clientsconfig.get(num_clients, 10), secure_aggregationTrue ) # 域管理器 self.domain_manager DomainManager() # 风险分析器 self.risk_analyzer RiskAnalyzer() # 性能监控 self.performance_monitor PerformanceMonitor() # 对抗性评估 self.adversarial_evaluator AdversarialEvaluator() # 状态缓存 self.state_cache {} self.action_history deque(maxlen1000) self.reward_history deque(maxlen1000) async def defend_domain(self, domain: str, threat_level: float 0.5, strategy: Optional[DefenseStrategy] None) - Dict[str, Any]: 防御域名 defense_start time.time() # 1. 获取当前状态 current_state await self._get_current_state(domain, threat_level) # 2. 策略选择 if strategy is None: strategy await self._select_strategy(current_state, threat_level) # 3. 生成对抗性流量 traffic_pattern self._map_strategy_to_pattern(strategy) adversarial_traffic await self.traffic_generator.generate_traffic( domaindomain, pattern_typetraffic_pattern, durationself.config.get(traffic_duration, 300), intensitythreat_level ) # 4. 执行防御 defense_result await self._execute_defense( domaindomain, strategystrategy, trafficadversarial_traffic, threat_levelthreat_level ) # 5. 评估结果 evaluation await self._evaluate_defense( domaindomain, strategystrategy, resultdefense_result, threat_levelthreat_level ) # 6. 计算奖励 reward self._calculate_reward(defense_result, evaluation) # 7. 学习 await self._learn_from_experience( statecurrent_state, actionstrategy, rewardreward, next_stateawait self._get_current_state(domain, threat_level), doneevaluation.get(complete, False) ) # 8. 元学习更新 if len(self.action_history) 10: await self._meta_learn_update() defense_duration time.time() - defense_start return { domain: domain, strategy: strategy.value, threat_level: threat_level, defense_result: defense_result, evaluation: evaluation, reward: reward, duration: defense_duration, timestamp: datetime.now().isoformat() } async def _get_current_state(self, domain: str, threat_level: float) - torch.Tensor: 获取当前状态 # 从缓存获取状态 cache_key f{domain}_{threat_level} if cache_key in self.state_cache: cached_state, timestamp self.state_cache[cache_key] if time.time() - timestamp 60: # 60秒缓存 return cached_state # 收集状态信息 state_features {} # 域名状态 domain_state self.domain_manager.get_domain_state(domain) state_features.update(domain_state) # 风险状态 risk_state await self.risk_analyzer.analyze_risk(domain, threat_level) state_features.update(risk_state) # 性能状态 performance_state self.performance_monitor.get_performance_stats(domain) state_features.update(performance_state) # 对抗状态 adversarial_state self.adversarial_evaluator.get_adversarial_state(domain) state_features.update(adversarial_state) # 转换为张量 feature_list [] for key in sorted(state_features.keys()): if isinstance(state_features[key], (int, float)): feature_list.append(float(state_features[key])) elif isinstance(state_features[key], bool): feature_list.append(1.0 if state_features[key] else 0.0) # 填充到固定长度 while len(feature_list) 256: feature_list.append(0.0) feature_list feature_list[:256] # 截断 state_tensor torch.tensor(feature_list, dtypetorch.float32) # 缓存状态 self.state_cache[cache_key] (state_tensor, time.time()) return state_tensor async def _select_strategy(self, state: torch.Tensor, threat_level: float) - DefenseStrategy: 选择策略 # 使用RL智能体选择策略 with torch.no_grad(): action_probs, value, uncertainty self.rl_agent(state.unsqueeze(0)) action_probs action_probs.squeeze(0) # 根据不确定性调整探索 exploration_rate self.config.get(exploration_rate, 0.1) uncertainty_bonus uncertainty.item() * 0.1 exploration_rate min(0.3, exploration_rate uncertainty_bonus) if random.random() exploration_rate: # 探索 action_idx random.randint(0, len(DefenseStrategy) - 1) else: # 利用 action_idx torch.argmax(action_probs).item() # 根据威胁级别调整策略 if threat_level 0.8: # 高威胁使用更积极的策略 aggressive_strategies [DefenseStrategy.AGGRESSIVE, DefenseStrategy.PROACTIVE] if random.random() 0.7: # 70%概率使用积极策略 return random.choice(aggressive_strategies) strategies list(DefenseStrategy) return strategies[action_idx] def _map_strategy_to_pattern(self, strategy: DefenseStrategy) - str: 策略到流量模式映射 mapping { DefenseStrategy.STEALTH: stealth, DefenseStrategy.EVASION: stealth, DefenseStrategy.CONFUSION: mimicry, DefenseStrategy.DECEPTION: mimicry, DefenseStrategy.ADAPTIVE: adaptive, DefenseStrategy.AGGRESSIVE: aggressive, DefenseStrategy.DIVERSIFIED: adaptive, DefenseStrategy.PROACTIVE: aggressive, DefenseStrategy.REACTIVE: recovery, DefenseStrategy.PREDICTIVE: adaptive } return mapping.get(strategy, adaptive) async def _execute_defense(self, domain: str, strategy: DefenseStrategy, traffic: List[Dict[str, Any]], threat_level: float) - Dict[str, Any]: 执行防御 execution_start time.time() # 执行流量 results [] success_count 0 total_count len(traffic) async with aiohttp.ClientSession() as session: tasks [] for request in traffic: task self._execute_request(session, request) tasks.append(task) # 限制并发 batch_size self.config.get(concurrent_requests, 10) for i in range(0, len(tasks), batch_size): batch tasks[i:ibatch_size] batch_results await asyncio.gather(*batch, return_exceptionsTrue) results.extend(batch_results) # 批量间延迟 if i batch_size len(tasks): await asyncio.sleep(random.uniform(0.5, 2.0)) # 分析结果 for result in results: if isinstance(result, dict) and result.get(success, False): success_count 1 success_rate success_count / total_count if total_count 0 else 0.0 # 计算指标 response_times [r.get(response_time, 0) for r in results if isinstance(r, dict)] avg_response_time np.mean(response_times) if response_times else 0.0 execution_time time.time() - execution_start return { strategy: strategy.value, total_requests: total_count, successful_requests: success_count, success_rate: success_rate, avg_response_time: avg_response_time, execution_time: execution_time, threat_level: threat_level, timestamp: datetime.now().isoformat(), detailed_results: results[:10] # 只返回前10个详细结果 } async def _execute_request(self, session: aiohttp.ClientSession, request: Dict[str, Any]) - Dict[str, Any]: 执行请求 start_time time.time() try: url request.get(url, ) headers request.get(headers, {}) async with session.get(url, headersheaders, timeoutaiohttp.ClientTimeout(total10)) as response: response_time time.time() - start_time is_success 200 response.status 400 is_blocked self._check_if_blocked(response) return { success: is_success and not is_blocked, status_code: response.status, response_time: response_time, url: str(response.url), headers: dict(response.headers), blocked: is_blocked, error: None } except asyncio.TimeoutError: return { success: False, error: timeout, response_time: time.time() - start_time } except Exception as e: return { success: False, error: str(e), response_time: time.time() - start_time } def _check_if_blocked(self, response) - bool: 检查是否被拦截 blocked_indicators [ (r微信安全中心, response.text, re.IGNORECASE), (r已停止访问该网页, response.text, re.IGNORECASE), (r此网页可能存在风险, response.text, re.IGNORECASE), (r访问受限, response.text, re.IGNORECASE), (r被投诉, response.text, re.IGNORECASE), (r违法违规, response.text, re.IGNORECASE), (r安全警告, response.text, re.IGNORECASE), (rblocked, response.url, re.IGNORECASE), (rforbidden, response.url, re.IGNORECASE), (403, response.status), (451, response.status), (503, response.status) ] for indicator in blocked_indicators: if len(indicator) 3: #