Internet of Things Intrusion Detection: Centralized, On-Device, or Federated Learning?
Abstract
With the ever increasing number of cyber-attacks, internet of Things (ioT) devices are being exposed to serious malware, attacks, and malicious activities alongside their development. While past research has been focused on centralized intrusion detection assuming the existence of a central entity to store and perform analysis on data from all participant devices, these approaches cannot scale well with the fast growth of ioT connected devices and introduce a single-point failure risk that may compromise data privacy. Moreover, with data being widely spread across large networks of connected devices, decentralized computations are very much in need. in this context, we propose in this article a Federated Learning based scheme for ioT intrusion detection that maintains data privacy by performing local training and inference of detection models. in this scheme, not only privacy can be assured, but also devices can benefit from their peers' knowledge by communicating only their updates with a remote server that aggregates the latter and shares an improved detection model with participating devices. We perform thorough experiments on an NSL-KDD dataset to evaluate the efficiency of the proposed approach. Experimental results and empirical analysis explore the robustness and advantages of the proposed Federated Learning detection model by reaching an accuracy close to that of the centralized approach and outperforming the distributed unaggregated on-device trained models.
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The proposed system has an accuracy fluctuating around 83.09% on the NSL-KDD dataset.
“The performance evaluation on an NSL-KDD dataset shows that the proposed system have an accuracy fluctuating around 83.09 %.”
From Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis (2021) · cites this paper as [91]
Cites
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215 · see citing works
A detailed analysis of the KDD CUP 99 data set
Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009) · 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications · doi:10.1109/cisda.2009.5356528 · Cited by 5,396 · see citing works
Distributed attack detection scheme using deep learning approach for Internet of Things
Diro, A. A., & Chilamkurti, N. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.08.043 · Cited by 1,100 · see citing works
Scheduling Policies for Federated Learning in Wireless Networks
Yang, H. H., Liu, Z., Quek, T. Q. S., & Poor, H. V. (2020) · IEEE Transactions on Communications · doi:10.1109/tcomm.2019.2944169 · Cited by 640 · see citing works
Semi-supervised learning based distributed attack detection framework for IoT
Rathore, S., & Park, J. H. (2018) · Applied Soft Computing · doi:10.1016/j.asoc.2018.05.049 · Cited by 355 · see citing works
A training-integrity privacy-preserving federated learning scheme with trusted execution environment
Chen, Y., Luo, F., Li, T., Xiang, T., Liu, Z., & Li, J. (2020) · Information Sciences · doi:10.1016/j.ins.2020.02.037 · Cited by 188 · see citing works
FDC: A Secure Federated Deep Learning Mechanism for Data Collaborations in the Internet of Things
Yin, B., Yin, H., Wu, Y., & Jiang, Z. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2966778 · Cited by 149 · see citing works
One-Shot Federated LearningPreprint
Guha, N., Talwalkar, A., & Smith, V. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.11175 · Cited by 137 · see citing works
Centralized Fog Computing Security Platform for IoT and Cloud in Healthcare System
Thota, C., Sundarasekar, R., Manogaran, G., et al. (2017) · Advances in data mining and database management book series · doi:10.4018/978-1-5225-2947-7.ch011 · Cited by 129 · see citing works
Centralized Fog Computing Security Platform for IoT and Cloud in Healthcare System
Thota, C., Sundarasekar, R., Manogaran, G., et al. (2018) · IGI Global eBooks · doi:10.4018/978-1-5225-5649-7.ch018 · Cited by 90 · see citing works
Zhao, S., Li, W., Zia, T. A., & Zomaya, A. Y. (2017) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc-picom-datacom-cyberscitec.2017.141 · Cited by 86 · see citing works
Hybrid-FL: Cooperative Learning Mechanism Using Non-IID Data in Wireless Networks.Preprint
Yoshida, N., Nishio, T., Morikura, M., Yamamoto, K., & Yonetani, R. (2019) · arXiv (Cornell University) · Cited by 37 · see citing works
Negative Selection and Neural Network Based Algorithm for Intrusion Detection in IoT
Pamukov, M. E., Poulkov, V. K., & Shterev, V. A. (2018) · International Conference on Telecommunications and Signal Processing (TSP) · doi:10.1109/tsp.2018.8441338 · Cited by 34 · see citing works
Chawla, S., & Thamilarasu, G. (2018) · doi:10.1145/3212687.3212872 · Cited by 22 · see citing works
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Abdulrahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., & Guizani, M. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3030072 · Cited by 796 · see citing works
Self-citationFederated Learning for intrusion detection system: Concepts, challenges and future directions
Agrawal, S., Sarkar, S., Aouedi, O., Yenduri, G., Piamrat, K., Alazab, M., Bhattacharya, S., Maddikunta, P. K. R., & Gadekallu, T. R. (2022) · Computer Communications · doi:10.1016/j.comcom.2022.09.012 · Cited by 457 · see citing works
Ferrag, M. A., Friha, O., Maglaras, L., Janicke, H., & Shu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3118642 · Cited by 342 · see citing works
Bashir, A. K., Victor, N., Bhattacharya, S., Huynh-The, T., Chengoden, R., Yenduri, G., Maddikunta, P. K. R., Pham, Q.-V., Gadekallu, T. R., & Liyanage, M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3304790 · Cited by 183 · see citing works
A blockchain-based federated learning mechanism for privacy preservation of healthcare IoT data
Moulahi, W., Jdey, I., Moulahi, T., Alawida, M., & Alabdulatif, A. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107630 · Cited by 103 · see citing works
Blockchain-Based Personalized Federated Learning for Internet of Medical Things
Lian, Z., Wang, W., Han, Z., & Su, C. (2023) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2023.3279111 · Cited by 75 · see citing works
Data-Agnostic Model Poisoning Against Federated Learning: A Graph Autoencoder Approach
Li, K., Zheng, J., Yuan, X., Ni, W., Akan, O. B., & Poor, H. V. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3362147 · Cited by 41 · see citing works
- Authors
- Rahman, S. A., Tout, H., Talhi, C., & Mourad, A.
- Affiliations
- École de Technologie Supérieure (CA)
- Lebanese American University (LB)
- Venue
- IEEE Network
- Published
- 2020-11
- Volume
- 34
- Issue
- 6
- Pages
- 310-317
- ISSN
- 0890-8044 (ISSN-L)
- 1558-156X
- OpenAlex
- W3082551002
- Cited by
- 414
- References
- 14
- Citations by year
- 2026: 44 · 2025: 94 · 2024: 85 · 2023: 107 · 2022: 50 · 2021: 31 · 2020: 3
- FWCI
- 22.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Network (publishedVersion)
- Espace ÉTS (ETS) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- Internet of Things, intrusion detection, federated learning, on-device learning, centralized learning, data privacy, decentralized machine learning, NSL-KDD dataset, Internet of Things security
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