Emilie Bout
ABSTRACT
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.
- Abbas Acar, Hossein Fereidooni, Tigist Abera, Amit Kumar Sikder, Markus Miettinen, Hidayet Aksu, Mauro Conti, Ahmad-Reza Sadeghi, and Selcuk Uluagac. 2020. Peek-a-boo. Proceedings of the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks (Jul 2020). https://doi.org/10.1145/3395351.3399421Google Scholar
Digital Library
- Salvatore Chiaravalloti, Filip Idzikowski, and Lukasz Budzisz. 2011. Power consumption of WLAN network elements. TKN Technical Reports Series (08 2011). https://doi.org/10.13140/2.1.4424.8005Google Scholar
- Atheros Communications. [n.d.]. Single-Chip 2x2 MIMO MAC/BB/Radio with PCI Express Interface for 802.11n 2.4 and 5 GHz WLANs. https://datasheetspdf.com/datasheet/AR9280.htmlGoogle Scholar
- Hiba Dakdouk, Erika Tarazona, Reda Alami, Raphaël Féraud, Georgios Z Papadopoulos, and Patrick Maillé. 2018. Reinforcement learning techniques for optimized channel hopping in IEEE 802.15. 4-TSCH networks. In Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems. 99–107.Google ScholarDigital Library
- Piotr Gawlowicz and Anatolij Zubow. 2019. ns-3 meets OpenAI Gym: The Playground for Machine Learning in Networking Research. 113–120. https://doi.org/10.1145/3345768.3355908Google ScholarDigital Library
- Aditya Grover, Todor Markov, Peter Attia, Norman Jin, Nicolas Perkins, Bryan Cheong, Michael Chen, Zi Yang, Stephen Harris, William Chueh, and Stefano Ermon. 2018. Best arm identification in multi-armed bandits with delayed feedback. In Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics(Proceedings of Machine Learning Research, Vol. 84), Amos Storkey and Fernando Perez-Cruz (Eds.). PMLR, 833–842.Google Scholar
- Kanika Grover, Alvin Lim, and Qing Yang. 2014. Jamming and anti–jamming techniques in wireless networks: a survey. International Journal of Ad Hoc and Ubiquitous Computing 17, 4(2014), 197–215.Google ScholarDigital Library
- Fengxian Guo, F Richard Yu, Heli Zhang, Xi Li, Hong Ji, and Victor CM Leung. 2021. Enabling massive IoT toward 6G: A comprehensive survey. IEEE Internet of Things Journal(2021).Google Scholar
- Guoan Han, Liang Xiao, and H Vincent Poor. 2017. Two-dimensional anti-jamming communication based on deep reinforcement learning. In 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 2087–2091.Google ScholarDigital Library
- Wan Haslina Hassan 2019. Current research on Internet of Things (IoT) security: A survey. Computer networks 148(2019), 283–294.Google Scholar
- S. Kawade, T. G. Hodgkinson, and V. Abhayawardhana. 2007. Interference Analysis of 802.11b and 802.11g Wireless Systems. In 2007 IEEE 66th Vehicular Technology Conference. 787–791. https://doi.org/10.1109/VETECF.2007.174Google ScholarCross Ref
- Network Security Lab. [n.d.]. Wireless jamming model. https://www.nsnam.org/wiki/Wireless_jamming_modelGoogle Scholar
- Eun-Kyu Lee, Soon Y Oh, and Mario Gerla. 2010. Randomized channel hopping scheme for anti-jamming communication. In 2010 IFIP Wireless Days. IEEE, 1–5.Google Scholar
- Fang Liu and Ness Shroff. 2019. Data Poisoning Attacks on Stochastic Bandits. In Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). 4042–4050.Google Scholar
- Xin Liu, Yuhua Xu, Luliang Jia, Qihui Wu, and Alagan Anpalagan. 2018. Anti-jamming communications using spectrum waterfall: A deep reinforcement learning approach. IEEE Communications Letters 22, 5 (2018), 998–1001.Google ScholarCross Ref
- N. Msadek, R. Soua, and T. Engel. 2019. IoT Device Fingerprinting: Machine Learning based Encrypted Traffic Analysis. In 2019 IEEE Wireless Communications and Networking Conference (WCNC). 1–8.Google Scholar
- Nima Namvar, Walid Saad, Niloofar Bahadori, and Brian Kelley. 2016. Jamming in the internet of things: A game-theoretic perspective. In 2016 IEEE Global Communications Conference (GLOBECOM). IEEE, 1–6.Google ScholarDigital Library
- Hossein Noori and Saeed Sadeghi Vilni. 2020. Jamming and anti-jamming in interference channels: a stochastic game approach. IET Communications 14, 4 (2020), 682–692.Google ScholarDigital Library
- Daniel Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband, and Zheng Wen. 2017. A tutorial on thompson sampling. arXiv preprint arXiv:1707.02038(2017).Google Scholar
- Michael Spuhler, Domenico Giustiniano, Vincent Lenders, Matthias Wilhelm, and Jens B. Schmitt. 2014. Detection of Reactive Jamming in DSSS-based Wireless Communications. IEEE Transactions on Wireless Communications 13, 3(2014), 1593–1603. https://doi.org/10.1109/TWC.2013.013014.131037Google ScholarCross Ref
- Xiao Tang, Pinyi Ren, and Zhu Han. 2018. Jamming mitigation via hierarchical security game for IoT communications. IEEE Access 6(2018), 5766–5779.Google ScholarCross Ref
- Viktor Toldov, Laurent Clavier, Valeria Loscrí, and Nathalie Mitton. 2016. A Thompson Sampling approach to channel exploration-exploitation problem in multihop cognitive radio networks. In 2016 IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). IEEE, 1–6.Google ScholarDigital Library
- Bikalpa Upadhyaya, Sumei Sun, and Biplab Sikdar. 2019. Machine learning-based jamming detection in wireless iot networks. In 2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS). IEEE, 1–5.Google ScholarCross Ref
- Daniel Vial, Sanjay Shakkottai, and R. Srikant. 2021. Robust Multi-Agent Multi-Armed Bandits. arxiv:2007.03812 [cs.LG]Google Scholar
- Feng Wang, M Cenk Gursoy, and Senem Velipasalar. 2021. Adversarial Reinforcement Learning in Dynamic Channel Access and Power Control. In 2021 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 1–6.Google Scholar
- Ximing Wang, Yuhua Xu, Jin Chen, Chunguo Li, Xin Liu, Dianxiong Liu, and Yifan Xu. 2020. Mean field reinforcement learning based anti-jamming communications for ultra-dense internet of things in 6G. In 2020 International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 195–200.Google ScholarCross Ref
- Liang Xiao, Xiaoyue Wan, Wei Su, Yuliang Tang, 2018. Anti-jamming underwater transmission with mobility and learning. IEEE Communications Letters 22, 3 (2018), 542–545.Google ScholarCross Ref
- Jianliang Xu, Huaxun Lou, Weifeng Zhang, and Gaoli Sang. 2020. An intelligent anti-jamming scheme for cognitive radio based on deep reinforcement learning. IEEE Access 8(2020), 202563–202572.Google ScholarCross Ref
- Wenyuan Xu, Wade Trappe, Yanyong Zhang, and Timothy Wood. 2005. The Feasibility of Launching and Detecting Jamming Attacks in Wireless Networks. In Proceedings of the 6th ACM International Symposium on Mobile Ad Hoc Networking and Computing. Association for Computing Machinery, 46–57. https://doi.org/10.1145/1062689.1062697Google ScholarDigital Library
- Wenyuan Xu, W. Trappe, Yanyong Zhang, and Timothy Wood. 2005. The Feasibility of Launching and Detecting Jamming Attacks in Wireless Networks. Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc). https://doi.org/10.1145/1062689.1062697Google ScholarDigital Library
- Chen Zhong, Feng Wang, M Cenk Gursoy, and Senem Velipasalar. 2020. Adversarial jamming attacks on deep reinforcement learning based dynamic multichannel access. In 2020 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 1–6.Google ScholarDigital Library
- Quan Zhou, Yonggui Li, and Yingtao Niu. 2021. Intelligent Anti-Jamming Communication for Wireless Sensor Networks: A Multi-Agent Reinforcement Learning Approach. IEEE Open Journal of the Communications Society 2 (2021), 775–784.Google ScholarCross Ref
Index Terms
- FOLPETTI: A Novel Multi-Armed Bandit Smart Attack for Wireless Networks
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