Multi-Stage Game-based Topology Deception Method Using Deep Reinforcement Learning
Aiming at the problem that current network topology deception methods only make decisions in the spatial dimension without considering how to perform spatio-temporal multi-dimensional topology deception in cloud-native network environments,a multi-stage Flipit game topology deception method with deep reinforcement learning to obfuscate reconnaissance attacks in cloud-native networks.Firstly,the topology deception defense-offense model in cloud-native complex network environments is analyzed.Then,by introducing a discount factor and transition probabilities,a multi-stage game-based network topology deception model based on Flipit is constructed.Furthermore under the premise of analyzing the defense-offense strategies of game models,a topology deception generation method is developed based on deep reinforcement learning to solve the topology deception strategy of multi-stage game models.Finally,through experiments,it is demonstrated that the proposed method can effectively model and analyze the topology deception defense-offense scenarios in cloud-native networks.It is shown that the algorithm has significant advantages compared to other algorithms.
万方数据
维普
