SoK: A Comparison of Autonomous Penetration Testing Agents

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In the still growing field of cyber security, machine learning methods have largely been employed for detection tasks. Only a small portion revolves around offensive capabilities. Through the rise of Deep Reinforcement Learning, agents have also emerged with the goal of actively assessing the security of systems by the means of penetration testing. Thus learning the usage of different tools to emulate humans. In this paper we present an overview, and comparison of different autonomous penetration testing agents found within the literature. Various agents have been proposed, making use of distinct methods, but several factors such as modelling of the environment and scenarios, different algorithms, and the difference in chosen methods themselves, make it difficult to draw conclusions on the current state and performance of those agents. This comparison also lets us identify research challenges that present a major limiting factor, such as handling large action spaces, partial observability, defining the right reward structure, and learning in a real-world scenario.

Original languageEnglish
Title of host publicationARES 2024 - 19th International Conference on Availability, Reliability and Security, Proceedings
PublisherAssociation for Computing Machinery
ISBN (Electronic)9798400717185
DOIs
Publication statusPublished - 30 Jul 2024
Event19th International Conference on Availability, Reliability and Security, ARES 2024 - Vienna, Austria
Duration: 30 Jul 20242 Aug 2024

Publication series

NameACM International Conference Proceeding Series

Conference

Conference19th International Conference on Availability, Reliability and Security, ARES 2024
Country/TerritoryAustria
CityVienna
Period30/07/242/08/24

Keywords

  • Deep Reinforcement Learning
  • Penetration Testing
  • Reinforcement Learning
  • Security Automation

Fingerprint

Dive into the research topics of 'SoK: A Comparison of Autonomous Penetration Testing Agents'. Together they form a unique fingerprint.

Cite this