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

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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 target defense (MTD) mechanisms in Internet of Things (IoT) networks is ongoing research, with unfathomable potential to equip IoT devices and networks with the ability to fend off cyber attacks despite their computational deficiencies. In this paper, we propose a game-theoretic model of MTD to render the configuration and deployment of anomaly-based IDS more dynamic through diversification of feature training in order to minimize successful reconnaissance on ML-based IDS. We then solve the MTD problem using a reinforcement learning method to generate the optimal shifting policy within the network without a prior network transition model. The state-of-the-art ToN-IoT dataset is investigated for feasibility to implement the feature-based MTD approach. The overall performance of the proposed MTD-based IDS is compared to a conventional IDS by analyzing the accuracy curve for varying attacker success rates. Our approach has proven effective in increasing the resilience of the IDS against adversarial learning.

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