Design and Analysis of Decentralized Interactive Cyber Defense Approach based on Multi-agent Coordination

Since the current cyberspace is becoming changeable and complex over times, the situation of cyber security is becoming increasingly severe, and one of the important issues is that there is still lack of a general applicability defense model for open and dynamic networks. Recent research suggests that the evolutionary game methods have the advantage of improving the defensive capabilities based on the internal decision and learning mechanisms. In this paper, by exploiting the advantage of collaborative decision-making in multi-agent system, we constructed the dynamic cyber defense problem into a decentralized multi-agent cooperative decision framework, whose core idea is the initiative decentralized interactions among defense agents. Then, we contributed a heuristic imprecise probabilistic based interaction decision algorithm, HIDS, that is, which utilizes the multidimensional semantic relevance among observation, tasks and agents, so that agents can continuously improve cognition and optimize decision-making by learning interactive records. In addition, we analyzed the equivalence and the transformation conditions between the proposed model and the existing decision models, and combined the evolutionary game with the nonlinear stochastic theory, then the evolution process of the defense policies are analyzed. Finally, the performance comparison of the proposed algorithm and the influence of different intensity random disturbances on the evolution process are analyzed.