Channel hopping is a well-known methodology to dynamically allocate frequency resources to nodes in wireless communication systems. The aim of channel hopping is to mitigate possible interference issues caused by both legitimate and malicious users. The state-of-the-art channel hopping-based jamming mitigation techniques are becoming increasingly smart by using game theory or machine learning. However, we show in this paper that, although the victim may employ smart channel hopping strategies, these are not always effective in mitigating attacks. In this paper, we implement an effective jamming attack strategy to impair the effectiveness of channel hopping mitigation strategies. We test our attack on a testbed network composed by real devices, and test its effectiveness against three mitigation strategies: i) incremental channel hopping, ii) random channel hopping, and iii) smart channel hopping. We show that, by implementing our attack, an attacker can reduce the number of successfully transmitted packets by at least 28.5%,

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 not received, therefore this represents the limit for the attack to be stealthy. In this scenario, FOLPETTI achieves a 15% success rate for the victim's random channel selection strategy, close to the 17.5% obtained with a genie-aided approach. Conversely, the DRL-based approach reaches a success rate of 12.5%, which is 5.5% less than FOLPETTI. We also confirm the results by confronting FOLPETTI with a MAB based channel hopping method. Finally, we show that FOLPETTI creates an additional energy demand independently from its success rate, therefore decreasing the lifetime of IoT devices.