53：顶级机构追踪防御：即使NSA级也无法定位的加密设计

import random
import time

class DynamicNetwork:
    def __init__(self, nodes):
        self.nodes = nodes
        self.current_topology = self.generate_topology()
        self.topology_change_interval = 300  # 5分钟
        self.last_topology_change = time.time()
    
    def generate_topology(self):
        """生成随机网络拓扑"""
        topology = {}
        for node in self.nodes:
            # 为每个节点随机选择3-5个邻居
            neighbors = random.sample([n for n in self.nodes if n != node], 
                                    random.randint(3, 5))
            topology[node] = neighbors
        return topology
    
    def get_route(self, source, destination):
        """获取动态路由"""
        # 检查是否需要更新拓扑
        if time.time() - self.last_topology_change > self.topology_change_interval:
            self.current_topology = self.generate_topology()
            self.last_topology_change = time.time()
        
        # 使用深度优先搜索查找路由
        route = self.dfs(source, destination, [])
        return route
    
    def dfs(self, current, destination, path):
        """深度优先搜索"""
        if current == destination:
            return path + [current]
        
        for neighbor in self.current_topology[current]:
            if neighbor not in path:
                new_path = self.dfs(neighbor, destination, path + [current])
                if new_path:
                    return new_path
        
        return None
    
    def send_data(self, source, destination, data):
        """发送数据"""
        route = self.get_route(source, destination)
        if not route:
            raise Exception("No route found")
        
        # 沿路由发送数据
        current_data = data
        for i in range(len(route) - 1):
            # 每跳都重新加密
            current_data = self.encrypt_data(current_data, route[i], route[i+1])
            # 发送到下一个节点
            self.send_to_node(route[i], route[i+1], current_data)
        
        return route
    
    def encrypt_data(self, data, source, destination):
        """加密数据"""
        # 使用源节点和目标节点的共享密钥加密
        # 实现细节...
        return encrypted_data
    
    def send_to_node(self, source, destination, data):
        """发送数据到节点"""
        # 实现细节...
        pass

from cryptography.hazmat.primitives.asymmetric import kyber
from cryptography.hazmat.primitives import serialization

class PostQuantumEncryption:
    def __init__(self):
        # 生成Kyber密钥对
        self.private_key = kyber.generate_private_key()
        self.public_key = self.private_key.public_key()
    
    def encrypt(self, data):
        """使用抗量子算法加密数据"""
        # 使用Kyber算法加密
        ciphertext = self.public_key.encrypt(data)
        return ciphertext
    
    def decrypt(self, ciphertext):
        """使用抗量子算法解密数据"""
        # 使用Kyber算法解密
        plaintext = self.private_key.decrypt(ciphertext)
        return plaintext
    
    def get_public_key(self):
        """获取公钥"""
        return self.public_key.public_bytes(
            encoding=serialization.Encoding.PEM,
            format=serialization.PublicFormat.SubjectPublicKeyInfo
        )
    
    def load_public_key(self, public_key_pem):
        """加载公钥"""
        self.public_key = serialization.load_pem_public_key(public_key_pem)

import tensorflow as tf
import numpy as np

class AIDefense:
    def __init__(self):
        # 加载预训练的攻击检测模型
        self.model = tf.keras.models.load_model('attack_detection_model.h5')
        self.attack_patterns = self.load_attack_patterns()
    
    def load_attack_patterns(self):
        """加载已知攻击模式"""
        # 加载已知的攻击模式
        return {
            'nsa_pattern_1': [...],
            'nsa_pattern_2': [...],
            # 更多攻击模式...
        }
    
    def detect_attack(self, network_traffic):
        """检测攻击"""
        # 提取特征
        features = self.extract_features(network_traffic)
        
        # 使用模型检测
        prediction = self.model.predict(np.array([features]))
        
        # 检查已知攻击模式
        for pattern_name, pattern in self.attack_patterns.items():
            if self.match_pattern(features, pattern):
                return True, pattern_name
        
        return prediction[0][0] > 0.9, "unknown_attack"
    
    def extract_features(self, network_traffic):
        """提取特征"""
        # 提取网络流量特征
        # 实现细节...
        return features
    
    def match_pattern(self, features, pattern):
        """匹配攻击模式"""
        # 匹配攻击模式
        # 实现细节...
        return False
    
    def respond_to_attack(self, attack_type):
        """应对攻击"""
        # 根据攻击类型采取相应的防御措施
        if attack_type == 'nsa_pattern_1':
            # 采取特定的防御措施
            self.block_suspicious_ips()
            self.change_encryption_keys()
        elif attack_type == 'nsa_pattern_2':
            # 采取其他防御措施
            self.isolate_compromised_nodes()
            self.purge_malicious_code()
        else:
            # 采取通用防御措施
            self.increase_security_level()
    
    def block_suspicious_ips(self):
        """阻止可疑IP"""
        # 实现细节...
        pass
    
    def change_encryption_keys(self):
        """更换加密密钥"""
        # 实现细节...
        pass
    
    def isolate_compromised_nodes(self):
        """隔离被感染节点"""
        # 实现细节...
        pass
    
    def purge_malicious_code(self):
        """清除恶意代码"""
        # 实现细节...
        pass
    
    def increase_security_level(self):
        """提高安全级别"""
        # 实现细节...
        pass

# 多层加密配置
encryption:
  layers:
    - algorithm: "aes-256-gcm"
      key_size: 256
    - algorithm: "ecc-521"
      key_size: 521
    - algorithm: "chacha20"
      key_size: 256
    - algorithm: "hash-chain"
      hash_function: "sha-512"

# 动态网络配置
network:
  topology_change_interval: 300
  min_neighbors: 3
  max_neighbors: 5
  routing_algorithm: "dynamic-dfs"

# 抗量子加密配置
post_quantum:
  algorithm: "kyber-1024"
  key_exchange: "ntru-hrss701"

# AI防御配置
ai_defense:
  model_path: "attack_detection_model.h5"
  detection_threshold: 0.9
  response_strategy: "adaptive"

# 物理隔离配置
physical_isolation:
  enabled: true
  air_gap: true
  secure_boot: true