Modeling Penetration Testing with Reinforcement Learning Using Capture-the-Flag Challenges: Trade-offs between Model-free Learning and A Priori Knowledge

TL;DR

This paper explores using reinforcement learning to automate penetration testing through capture-the-flag challenges, highlighting the trade-offs between model-free methods and prior knowledge to improve efficiency.

Contribution

It demonstrates how injecting prior knowledge into reinforcement learning agents can enhance penetration testing effectiveness by balancing exploration and guidance.

Findings

Prior knowledge improves learning efficiency

Trade-off between model-free and model-based algorithms

Reinforcement learning can automate simplified penetration tests

Abstract

Penetration testing is a security exercise aimed at assessing the security of a system by simulating attacks against it. So far, penetration testing has been carried out mainly by trained human attackers and its success critically depended on the available expertise. Automating this practice constitutes a non-trivial problem, as the range of actions that a human expert may attempts against a system and the range of knowledge she relies on to take her decisions are hard to capture. In this paper, we focus our attention on simplified penetration testing problems expressed in the form of capture the flag hacking challenges, and we analyze how model-free reinforcement learning algorithms may help to solve them. In modeling these capture the flag competitions as reinforcement learning problems we highlight that a specific challenge that characterize penetration testing is the problem of discovering the structure of the problem at hand. We then show how this challenge may be eased by relying on different forms of prior knowledge that may be provided to the agent. In this way we demonstrate how the feasibility of tackling penetration testing using reinforcement learning may rest on a careful trade-off between model-free and model-based algorithms. By using techniques to inject a priori knowledge, we show it is possible to better direct the agent and restrict the space of its exploration problem, thus achieving solutions more efficiently.