Multi-Stage Game-based Topology Deception Method Using Deep Reinforcement Learning

HE Weizhen; TAN Jinglei; ZHANG Shuai; CHENG Guozhen; ZHANG Fan; GUO Yunfei

doi:10.11999/JEIT240029

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HE Weizhen, TAN Jinglei, ZHANG Shuai, CHENG Guozhen, ZHANG Fan, GUO Yunfei. Multi-Stage Game-based Topology Deception Method Using Deep Reinforcement Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240029

Citation:

HE Weizhen, TAN Jinglei, ZHANG Shuai, CHENG Guozhen, ZHANG Fan, GUO Yunfei. Multi-Stage Game-based Topology Deception Method Using Deep Reinforcement Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240029

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Multi-Stage Game-based Topology Deception Method Using Deep Reinforcement Learning

doi: 10.11999/JEIT240029

1.
Institute of Information Technology Research, Information Engineering University, Zhengzhou 450001, China
2.
Key Laboratory of Cyberspace Security, Ministry of Education, Zhengzhou 45000, China

Funds: Major Science and Technology Project of Henan Province in China (221100211200)

Received Date: 2024-01-19
Rev Recd Date: 2024-11-06

Available Online: 2024-11-12

Abstract

Abstract

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. Furthermore is constructed, 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 the topology deception defense-offense scenarios in cloud-native networks is analyzed. It is shown that the algorithm has significant advantages compared to other algorithms.
- Cloud-native network,
- Topology deception,
- Multi-stage Flipit game,
- Deep reinforcement learning,
- Deep deterministic policy gradient

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References(18)

References

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