FOLPETTI: A Novel Multi-Armed Bandit Smart Attack for Wireless Networks

TL;DR

FOLPETTI is a new multi-armed bandit attack that efficiently predicts and jams wireless channels in IoT networks, outperforming DRL-based methods and impacting device energy consumption.

Contribution

This paper introduces FOLPETTI, a real-time, continuous MAB-based attack that does not require recurrent training, unlike prior DRL-based approaches.

Findings

FOLPETTI achieves a 15% success rate against random channel selection.

It outperforms DRL-based attacks with a 12.5% success rate.

FOLPETTI increases energy consumption, reducing IoT device lifetime.

Abstract

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.