Paper ID: 2211.03027

Exposing Surveillance Detection Routes via Reinforcement Learning, Attack Graphs, and Cyber Terrain

Lanxiao Huang, Tyler Cody, Christopher Redino, Abdul Rahman, Akshay Kakkar, Deepak Kushwaha, Cheng Wang, Ryan Clark, Daniel Radke, Peter Beling, Edward Bowen

Reinforcement learning (RL) operating on attack graphs leveraging cyber terrain principles are used to develop reward and state associated with determination of surveillance detection routes (SDR). This work extends previous efforts on developing RL methods for path analysis within enterprise networks. This work focuses on building SDR where the routes focus on exploring the network services while trying to evade risk. RL is utilized to support the development of these routes by building a reward mechanism that would help in realization of these paths. The RL algorithm is modified to have a novel warm-up phase which decides in the initial exploration which areas of the network are safe to explore based on the rewards and penalty scale factor.

Submitted: Nov 6, 2022