论文标题
通过增强学习和攻击图的分层参考模型
A Layered Reference Model for Penetration Testing with Reinforcement Learning and Attack Graphs
论文作者
论文摘要
本文认为，使用攻击图从系统角度来看，在现实世界应用中使用攻击图来自动化渗透测试的关键挑战。 RL进行自动渗透测试的方法正在积极开发，但是对与RL相互作用的计算机网络的表示没有共识。此外，对于如何将这些表示形式基于应用RL解决方案方法的真实网络而言，存在重大的开放挑战。本文使用主题挑战与实时互动，模拟现实的对手行为以及处理不稳定的，不断发展的网络的主题挑战对代表和扎根进行了详细说明。这些挑战既实用又数学，它们直接涉及渗透测试系统的可靠性和可靠性。本文提出了一个分层的参考模型，以帮助组织相关的研究和工程工作。提出的分层参考模型将攻击图工作流的传统模型进行了对比，因为它不是范围内的，而是依次，而是馈送前进的生成和分析过程，而是范围内的，而是范围内的，而是范围更广泛的生命周期和连续部署的方面。研究人员和从业人员可以将呈现的分层参考模型用作第一原理大纲，以帮助确定其渗透测试系统的系统工程。
This paper considers key challenges to using reinforcement learning (RL) with attack graphs to automate penetration testing in real-world applications from a systems perspective. RL approaches to automated penetration testing are actively being developed, but there is no consensus view on the representation of computer networks with which RL should be interacting. Moreover, there are significant open challenges to how those representations can be grounded to the real networks where RL solution methods are applied. This paper elaborates on representation and grounding using topic challenges of interacting with real networks in real-time, emulating realistic adversary behavior, and handling unstable, evolving networks. These challenges are both practical and mathematical, and they directly concern the reliability and dependability of penetration testing systems. This paper proposes a layered reference model to help organize related research and engineering efforts. The presented layered reference model contrasts traditional models of attack graph workflows because it is not scoped to a sequential, feed-forward generation and analysis process, but to broader aspects of lifecycle and continuous deployment. Researchers and practitioners can use the presented layered reference model as a first-principles outline to help orient the systems engineering of their penetration testing systems.
