An Intelligent Route Mutation Mechanism against Mixed Attack Based on Security Awareness
Tao Zhang, Xiaohui Kuang, Zan Zhou, Hongquan Gao, Changqiao Xu · 2019
Static network defense technologies are always in a passive defense state because of their disadvantages in cost, time and information. So Network Moving Target Defense (NMTD) is proposed as a kind of proactive defense technology. As an important research direction of NMTD, route mutation techniques still have limitations that they can not learn attack strategies and be adaptive in dynamical security situation. In this paper, we propose a novel route mutation mechanism based on reinforcement learning. We firstly investigate four different attack strategies and introduce a mixed attack strategy with entropy constraints. Then we formulate the network requirements using Satisfiability Module Theory (SMT) logic to acquire the route mutation space. We further propose a security-awareness Q- learning algorithm to select routes from the mutation space iteratively and conduct security awareness to adjust learning rate adaptively. Meanwhile, the optimal convergence of our algorithm is proved theoretically. Finally, experimental results highlight the effectiveness as defense performance, network overhead and convergence speed of our method compared to the representative solution.