Reinforcement Learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases reliability.Nonetheless,given the rapidly expanding scale of modern network infrastructure,the...Reinforcement Learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases reliability.Nonetheless,given the rapidly expanding scale of modern network infrastructure,the limited testing scale and monotonous strategies of existing RLbased automated penetration testing methods make them less effective in practical application.In this paper,we present CLAP(Coverage-Based Reinforcement Learning to Automate Penetration Testing),an RL penetration testing agent that provides comprehensive network security assessments with diverse adversary testing behaviours on a massive scale.CLAP employs a novel neural network,namely the coverage mechanism,to address the enormous and growing action spaces in large networks.It also utilizes a Chebyshev decomposition critic to identify various adversary strategies and strike a balance between them.Experimental results across various scenarios demonstrate that CLAP outperforms state-of-the-art methods,by further reducing attack operations by nearly 35%.CLAP also provides enhanced training efficiency and stability and can effectively perform pen-testing over large-scale networks with up to 500 hosts.Additionally,the proposed agent is also able to discover pareto-dominant strategies that are both diverse and effective in achieving multiple objectives.展开更多
With the continuous improvement of supercomputer performance and the integration of artificial intelligence with traditional scientific computing,the scale of applications is gradually increasing,from millions to tens...With the continuous improvement of supercomputer performance and the integration of artificial intelligence with traditional scientific computing,the scale of applications is gradually increasing,from millions to tens of millions of computing cores,which raises great challenges to achieve high scalability and efficiency of parallel applications on super-large-scale systems.Taking the Sunway exascale prototype system as an example,in this paper we first analyze the challenges of high scalability and high efficiency for parallel applications in the exascale era.To overcome these challenges,the optimization technologies used in the parallel supporting environment software on the Sunway exascale prototype system are highlighted,including the parallel operating system,input/output(I/O)optimization technology,ultra-large-scale parallel debugging technology,10-million-core parallel algorithm,and mixed-precision method.Parallel operating systems and I/O optimization technology mainly support largescale system scaling,while the ultra-large-scale parallel debugging technology,10-million-core parallel algorithm,and mixed-precision method mainly enhance the efficiency of large-scale applications.Finally,the contributions to various applications running on the Sunway exascale prototype system are introduced,verifying the effectiveness of the parallel supporting environment design.展开更多
基金supported by te Key Research Project of Zhejiang Lab(No.2021PB0AV02)。
文摘Reinforcement Learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases reliability.Nonetheless,given the rapidly expanding scale of modern network infrastructure,the limited testing scale and monotonous strategies of existing RLbased automated penetration testing methods make them less effective in practical application.In this paper,we present CLAP(Coverage-Based Reinforcement Learning to Automate Penetration Testing),an RL penetration testing agent that provides comprehensive network security assessments with diverse adversary testing behaviours on a massive scale.CLAP employs a novel neural network,namely the coverage mechanism,to address the enormous and growing action spaces in large networks.It also utilizes a Chebyshev decomposition critic to identify various adversary strategies and strike a balance between them.Experimental results across various scenarios demonstrate that CLAP outperforms state-of-the-art methods,by further reducing attack operations by nearly 35%.CLAP also provides enhanced training efficiency and stability and can effectively perform pen-testing over large-scale networks with up to 500 hosts.Additionally,the proposed agent is also able to discover pareto-dominant strategies that are both diverse and effective in achieving multiple objectives.
基金Project supported by the Key R&D Program of Zhejiang Province,China(No.2022C01250)the National Key R&D Program of China(No.2019YFA0709402)。
文摘With the continuous improvement of supercomputer performance and the integration of artificial intelligence with traditional scientific computing,the scale of applications is gradually increasing,from millions to tens of millions of computing cores,which raises great challenges to achieve high scalability and efficiency of parallel applications on super-large-scale systems.Taking the Sunway exascale prototype system as an example,in this paper we first analyze the challenges of high scalability and high efficiency for parallel applications in the exascale era.To overcome these challenges,the optimization technologies used in the parallel supporting environment software on the Sunway exascale prototype system are highlighted,including the parallel operating system,input/output(I/O)optimization technology,ultra-large-scale parallel debugging technology,10-million-core parallel algorithm,and mixed-precision method.Parallel operating systems and I/O optimization technology mainly support largescale system scaling,while the ultra-large-scale parallel debugging technology,10-million-core parallel algorithm,and mixed-precision method mainly enhance the efficiency of large-scale applications.Finally,the contributions to various applications running on the Sunway exascale prototype system are introduced,verifying the effectiveness of the parallel supporting environment design.
