TY - GEN
T1 - FOLPETTI
T2 - 17th International Conference on Availability, Reliability and Security, ARES 2022
AU - Bout, Emilie
AU - Brighente, Alessandro
AU - Conti, Mauro
AU - Loscri, Valeria
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Channel hopping
KW - Internet of Things
KW - Jamming
UR - http://www.scopus.com/inward/record.url?scp=85136944344&partnerID=8YFLogxK
U2 - 10.1145/3538969.3539001
DO - 10.1145/3538969.3539001
M3 - Conference contribution
AN - SCOPUS:85136944344
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 17th International Conference on Availability, Reliability and Security, ARES 2022
PB - Association for Computing Machinery (ACM)
Y2 - 23 August 2022 through 26 August 2022
ER -