[2T1-GS-10] AI application

[2T1-GS-10-05] A penetration testing method based on deep reinforcement learning

〇Tomonori Yoneda¹, Otsuka Akira¹ (1. INSTITUTE of INFORMATION SECURITY)

[[Online]]

Keywords:Deep reinforcement learning, Penetration test, POMDP

Penetration testing assesses the vulnerability of a target system by exhaustively attempting known attacks. Efficient penetration testers carefully investigate the target system to minimize attack attempts. In this study, we propose a penetration tester based on a neural agent that efficiently finds the optimal penetration strategy using deep reinforcement learning based on partially
observed Markov decision processes (POMDPs). In addition, while the baseline neural agents are based on GRUs, we propose a system with a variant of Transformer, called GTrXL, which is expected to solve the state prediction problem in neural agents from partially observed text using NLP techniques.
We have conducted several experiments against real linux-based systems through a wide-known autonomous penetration testing tool, called DeepExploit, as a part of the environment. We have succeeded in demonstrating the superiority of our proposed GTrXL-based agents against cutting-edge previous studies