Moving Target Defense Strategy Optimization Scheme for Cloud NativeEnvironment Based on Deep Reinforcement Learning br

To deal with the difficulty of configuring Moving Target Defense (MTD) strategy under complexityattack scenarios in the cloud native environment, a deep reinforcement learning based moving target defensestrategy optimization scheme (SmartSCR) is proposed. First, the security threats together with the attackpaths are analyzed considering the characteristics of containerization and microservice. Then, in order toevaluate the defense efficiency of moving target defense under complexity attack scenarios in the cloud nativeenvironment, the microservice attack graph model is proposed to defense quantify efficiency. Finally, theoptimization of moving target defense strategy is modeled as a Markov decision process. A deep reinforcementlearning based strategy is proposed to handle the state space explosion under large scale cloud nativeapplications, thus to solve out the optimal configuration for moving target defense strategy. The experimentresults show that SmartSCR can quickly converge under large scale cloud native applications, and achieve nearoptimal defense efficiency