Cyber resilience using autonomous agents and reinforcement learning

Hasan Cam

doi:10.1117/12.2559319

24 April 2020 Cyber resilience using autonomous agents and reinforcement learning

Hasan Cam

Proceedings Volume 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II; 114130R (2020) https://doi.org/10.1117/12.2559319
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Cyber resilience usually refers to the ability of an entity to detect, respond to, and recover from cybersecurity attacks to the extent that the entity can continuously deliver the intended outcome despite their presence. This paper presents a method and system for providing cyber resilience by integrating autonomous adversary and defender agents, deep reinforcement learning, and graph thinking. Specifically, the proposed cyber resilience system first predicts the current and future adversary activities and then provides an automated critical asset protection and recovery by enabling agents to take appropriate reactive and pro-active actions for preventing and mitigating adversary activities. In particular, the automated cyber resilience system’s adversary agent makes it possible for cybersecurity adversary activities, patterns, and intentions to be identified and tracked more accurately and dynamically, based on the preprocessed cybersecurity measurements and observations. The automated system’s defender agent is designed to determine and execute cost-effective defensive actions against the adversary activities and intentions predicted by the adversary agent. The game of these adversary and defender agents employ deep reinforcement learning to play a zero-sum observations-aware stochastic game. The experiment results show that the agents perform their tasks efficiently, as the adversary agent is dynamically provided with the input data of infected asset predictions.

Conference Presentation

Citation Download Citation

Hasan Cam "Cyber resilience using autonomous agents and reinforcement learning", Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 114130R (24 April 2020); https://doi.org/10.1117/12.2559319

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