Deep Reinforcement Learning Approaches Against Jammers with Unequal Sweeping Probability Attacks

Nguyen, Lan; Nguyen, Duy; Tran, Nghi; Brunnenmeyer, David

Deep Reinforcement Learning Approaches Against Jammers with Unequal Sweeping Probability Attacks

Nguyen, Lan and Nguyen, Duy and Tran, Nghi and Brunnenmeyer, David (2025) Deep Reinforcement Learning Approaches Against Jammers with Unequal Sweeping Probability Attacks. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems.

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Abstract

This paper investigates deep reinforcement learning (DRL) approaches designed to counter jammers that maximize disruption by employing unequal sweeping probabilities. We first propose a model and defense action based on a Markov Decision Process (MDP) under non-uniform attacks. A key drawback of the

Item Type:	Article
Date Deposited:	04 Mar 2026 20:21
Last Modified:	10 Apr 2026 17:44
URI:	http://eprints.eai.eu/id/eprint/60354

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