POMDP + Information-Decay: Incorporating Defender's Behaviour in Autonomous Penetration Testing

Author:

Schwartz Jonathon,Kurniawati Hanna,El-Mahassni Edwin

Abstract

Penetration testing (pen-testing) aims to assess vulnerabilities in a computer network by emulating possible attacks. Autonomous pen-testing allows frequent and regular pen-testing to be performed, which is increasingly necessary as networks become larger and more complex. Autonomous pen-testing is a planning under uncertainty problem, where the uncertainty is caused by partial observability of the network, lack of reliability of attack tools, and possible changes in the network that are triggered by the network administrator (the defender). Approaches that account for the first two causes of uncertainty have been developed based on the mathematically principled framework, Partially Observable Markov Decision Process (POMDP). However, they do not account for the third type of uncertainty. On the other hand, work that accounts for the defender's actions do not account for both partial observability and unreliability of the attack tools. This paper proposes a POMDP-based autonomous pen-testing framework that accounts for the defender's behaviour, thereby accounting for all of the above three causes of uncertainty. Key to our model is the observation that the defender's actions can be abstracted into two types: Network analysis, which does not alter the network, and active defence operations, which alter the network. This observation enables us to represent the defender's behaviour as a single variable: An information decay factor. This variable is based on the expected time the defender takes to move from analysing to actively defending the network, and therefore represents the decay of a pen-tester's knowledge about the network. We propose D-PenTesting, which assumes the decay factor is known prior to execution, and LD-PenTesting, which learns the decay factor as it attempts to break into the network. Simulation tests on two benchmark scenarios indicate that D-PenTesting and LD-PenTesting outperform existing POMDP-based pen-tester and is more robust than one that incorporates a POMDP-based defender.

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3