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

arXiv: https://arxiv.org/abs/2205.03915 · HTML: https://mlsanity.com/paper/2205.03915/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2022.

## Built on by (most cited first, 1 of 1)

- [Model-Adaptive and Risk-Constrained Frequency Hopping Against Predictive Jammers](https://mlsanity.com/paper/2609.06514/) (2026)

## Abstract (arXiv)

Channel hopping provides a defense mechanism against jamming attacks in large scale aciot 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.