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.
维普
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