Mitigating Adversarial Reconnaissance in IoT Anomaly Detection Systems: A Moving Target Defense Approach based on Reinforcement Learning

Osei, Arnold; Mtawa, Yaser Al; Halabi, Talal

Mitigating Adversarial Reconnaissance in IoT Anomaly Detection Systems: A Moving Target Defense Approach based on Reinforcement Learning

Osei, Arnold and Mtawa, Yaser Al and Halabi, Talal (2024) Mitigating Adversarial Reconnaissance in IoT Anomaly Detection Systems: A Moving Target Defense Approach based on Reinforcement Learning. EAI Endorsed Transactions on Internet of Things.

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Abstract

The machine learning (ML) community has extensively studied adversarial threats on learning-based systems, emphasizing the need to address the potential compromise of anomaly-based intrusion detection systems (IDS) through adversarial attacks. On the other hand, investigating the use of moving targe

Item Type:	Article
Date Deposited:	04 Mar 2026 18:15
Last Modified:	11 Apr 2026 00:09
URI:	http://eprints.eai.eu/id/eprint/51794

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