基于攻击图的强化学习王冠珠宝分析
Crown Jewels Analysis using Reinforcement Learning with Attack Graphs
摘要：
网络攻击对国家和企业构成了生存威胁。目前的做法倾向于使用威胁模型进行逐段分析，而不是严格的网络地形分析和战场情报准备。使用强化学习的自动渗透测试为开发由网络结构和网络地形驱动的方法提供了一种新的和有前途的方法，这些方法可以在以后根据威胁模型进行解释，但主要是网络驱动的分析。本文提出了一种称为CJA-RL的皇冠宝石分析的新方法，该方法使用强化学习来识别开发皇冠宝石的关键地形和途径。在我们的实验中，CJA-RL确定了利用具有多个皇冠宝石的网络的理想入口点，瓶颈点和枢轴，举例说明了CJA-RL和用于渗透测试的强化学习通常可以使计算机网络操作工作流程受益。
英文摘要：
Cyber attacks pose existential threats to nations and enterprises. Current practice favors piece-wise analysis using threat-models in the stead of rigorous cyber terrain analysis and intelligence preparation of the battlefield. Automated penetration testing using reinforcement learning offers a new and promising approach for developing methodologies that are driven by network structure and cyber terrain, that can be later interpreted in terms of threat-models, but that are principally network-driven analyses. This paper presents a novel method for crown jewel analysis termed CJA-RL that uses reinforcement learning to identify key terrain and avenues of approach for exploiting crown jewels. In our experiment, CJA-RL identified ideal entry points, choke points, and pivots for exploiting a network with multiple crown jewels, exemplifying how CJA-RL and reinforcement learning for penetration testing generally can benefit computer network operations workflows.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
