Research on multidimensional dynamic defense strategy for microservice based on deep reinforcement learning - Dacheng ZHOU; Hongchang CHEN; Weizhen HE; Guozhen CHENG; Hongchao HU (Tongxin xuebao)
Aiming at the problem that it is hard for security defense strategies in cloud native to guarantee the quality of service under dynamic requests, a multidimensional dynamic defense strategy for microservice based on deep reinforcement learning, named D2RA strategy, was proposed to provide dynamic configuration schemes that ensure security defense performance and quality of service for microservices under dynamical requests.Firstly, based on the characteristics of multiple replicas and invocation chains of microservices, a microservices state graph was established to depict the maps between requests, system configuration and security performance, quality of service, and resource overhead of microservices.Secondly, the D2RA framework was designed and a dynamic strategy optimization algorithm based on deep Q-network was proposed for microservices to provide fast and optimal system configurations update scheme under dynamic requests.The simulation results show that D2RA effectively allocate resources under dynamic requests, and achieve 19.07% more defense effectiveness and 42.31% higher quality of service as compared to the existing methods.