  Channel hopping provides a defense mechanism against jamming attacks in large
scale \ac{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 MAB-based attack to dynamically
follow the victim's channel selection in real-time. Compared to previous
attacks implemented via 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.
