Development of autonomous cyber system defense strategies and action recommendations in the real-world is challenging, and includes characterizing system state uncertainties and attack-defense dynamics. We propose a data-driven deep reinforcement learning (DRL) framework to learn proactive, context-aware, defense countermeasures that dynamically adapt to evolving adversarial behaviors while minimizing loss of cyber system operations. A dynamic defense optimization problem is formulated with multiple protective postures against different types of adversaries with varying levels of skill and persistence. A custom simulation environment was developed and experiments were devised to systematically evaluate the performance of four model-free DRL algorithms against realistic, multi-stage attack sequences. Our results suggest the efficacy of DRL algorithms for proactive cyber defense under multi-stage attack profiles and system uncertainties.
@article{arxiv.2302.01595,
title = {Deep Reinforcement Learning for Cyber System Defense under Dynamic Adversarial Uncertainties},
author = {Ashutosh Dutta and Samrat Chatterjee and Arnab Bhattacharya and Mahantesh Halappanavar},
journal= {arXiv preprint arXiv:2302.01595},
year = {2023}
}