Deep Reinforcement Learning for Cyber System Defense under Dynamic Adversarial Uncertainties

2023/02/03 by Dutta, Ashutosh, Chatterjee, Samrat, Bhattacharya, Arnab +1 · 2 citations
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multiagent Systems (cs.MA)

paper · doi:10.48550/arxiv.2302.01595

Abstract

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.

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• Large Language Model-Based Reward Design for Deep Reinforcement Learning-Driven Autonomous Cyber Defense
• Towards Production-Worthy Simulation for Autonomous Cyber Operations

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