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Channel hopping provides a defense mechanism against jamming attacks in large scale Internet of Things (IoT) networks. However, a sufficiently powerful attacker may be able to learn the channel hopping pattern and efficiently predict the channel to jam.

In this paper, we present FOLPETTI, a Multi-Armed Bandit (MAB)-based attack to dynamically follow the victim’s channel selection in real-time. Compared to previous attacks implemented via Deep Reinforcement Learning (DRL), FOLPETTI does not require recurrent training phases to capture the victim’s behavior, allowing hence a continuous attack. We assess the validity of FOLPETTI by implementing it to launch a jamming attack. We evaluate its performance against a victim performing random channel selection and a victim implementing a MAB defence strategy. We assume that the victim detects an attack when more than 20% of the transmitted packets are …