A Deep Reinforcement Learning-Based Deception Asset Selection Algorithm in Differential Games

Currently, there are various problems in the field of network attack-defense analysis and deception asset deployment of game theory-based, such as difficulties in constructing attack and defense models and determining real-time attack and defense strategies. To address these problems, this study proposes a differential game deception asset selection algorithm based on multi-agent deep reinforcement learning. Specifically, by analyzing the attack and defense strategies, the infectious disease model is developed to conduct the evolution analysis of the network security state, and the differential equation of the node state in the deception defense system is derived. In addition, a differential game model for the cyber deception attack-defense process is constructed, and the reward functions of the attacker and defender are designed. A deception asset selection algorithm is established based on the deep Q network method to solve optimal deception assets. The effectiveness of the proposed model is validated through a microservices attack-defense example in a cloud-native environment. The results show that compared to the deception asset selection algorithms based on the Fictitious Self Play and Policy Space Response Oracles, the convergence speed of the proposed algorithm is improved by 77.8% and 95.6%, respectively.