Abstract
The increasing adoption and use of IoT devices in smart home environments have raised concerns around the data security or privacy of smart home users. Several studies employed traditional machine learning to address the key security challenge, namely anomaly detection in IoT devices. Such models, however, require transmitting sensitive IoT data to a central model for training and validation which introduces security and performance concerns. In this paper, we propose a federated learning approach for detecting anomalies in IoT devices. We present our FedGroup model and algorithms that train and validate local models based on data from a group of IoT devices. FedGroup also updates the learning of the central model based on the learning changes that result from each group of IoT devices, rather than computing the average learning of each device. Our empirical evaluation of the real IoT dataset demonstrates the capability of our FedGroup model and anomaly detection accuracy as the same or better than federated and non-federated learning models. FedGroup is also more secure and performs well given all the IoT data are used to train and update the models locally.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Evans, D.: How the Next Evolution of the Internet Is Changing Everything. 11 (2011)
Robles, R.J., Kim, T.: Applications, systems and methods in smart home technology: a review. Int. J. Adv. Sci. Technol. 15, 13 (2010)
Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of things (IoT): a vision, architectural elements, and future directions. Futur. Gener. Comput. Syst. 29(7), 1645–1660 (2013). https://doi.org/10.1016/j.future.2013.01.010
Abomhara, M., Køien, G.M.: Security and privacy in the Internet of Things: Current status and open issues. 8
59 per cent of smart speaker users have privacy concerns - report|Mobile Marketing Magazine. Mobilemarketingmagazine.com. (2021). Accessed 23 Oct 2021. https://mobilemarketingmagazine.com/59-per-cent-of-smart-speaker-users-have-privacy-concerns-report
Habibi Gharakheili, H., Sivanathan, A., Hamza, A., Sivaraman, V.: Network-level security for the internet of things: opportunities and challenges. Computer 52(8), 58–62 (2019). https://doi.org/10.1109/MC.2019.2917972
Hamza, A., Gharakheili, H.H., Benson, T.A., Sivaraman, V.: Detecting Volumetric Attacks on loT devices via SDN-based monitoring of MUD activity. In: Proceedings of the 2019 ACM Symposium on SDN Research, pp. 36–48 (2019). https://doi.org/10.1145/3314148.3314352
Sivanathan, A., et al.: Classifying IoT devices in smart environments using network traffic characteristics. IEEE Trans. Mob. Comput. 18(8), 1745–1759 (2019). https://doi.org/10.1109/TMC.2018.2866249
Sivaraman, V., Gharakheili, H.H., Fernandes, C., Clark, N., Karliychuk, T.: Smart IoT devices in the home: security and privacy implications. IEEE Technol. Soc. Mag. 37(2), 71–79 (2018). https://doi.org/10.1109/MTS.2018.2826079
Stojkoska, B.L.R., Trivodaliev, K.V.: A review of internet of things for smart home: challenges and solutions. J. Clean. Prod. 140, 1454–1464 (2017)
Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated Machine Learning: Concept and Applications. ArXiv:1902.04885 (2019). http://arxiv.org/abs/1902.04885
McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.Y.: Communication-efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics, pp. 1273–1282 (2017). http://proceedings.mlr.press/v54/mcmahan17a.html
Rahman, S.A., Tout, H., Talhi, C., Mourad, A.: Internet of things intrusion detection: centralized, on-device, or federated learning? IEEE Netw. 34(6), 310–317 (2020). https://doi.org/10.1109/MNET.011.2000286
Mohri, M., Sivek, G., Suresh, A.T.: Agnostic Federated Learning. 11 (2019)
Li, T., Sanjabi, M., Beirami, A., Smith, V.: Fair Resource Allocation in Federated Learning. ArXiv:1905.10497 [Cs, Stat] (2020). http://arxiv.org/abs/1905.10497
Vanerio, J., Casas, P.: Ensemble-learning approaches for network security and anomaly detection. In: Proceedings of the Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, pp. 1–6 (2017).https://doi.org/10.1145/3098593.3098594
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, Y., Suleiman, B., Alibasa, M.J. (2023). FedGroup: A Federated Learning Approach for Anomaly Detection in IoT Environments. In: Longfei, S., Bodhi, P. (eds) Mobile and Ubiquitous Systems: Computing, Networking and Services. MobiQuitous 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 492. Springer, Cham. https://doi.org/10.1007/978-3-031-34776-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-34776-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34775-7
Online ISBN: 978-3-031-34776-4
eBook Packages: Computer ScienceComputer Science (R0)