Blockchain based federated learning for intrusion detection for Internet of Things

Abstract

In Internet of Things (IoT), data sharing among different devices can improve manufacture efficiency and reduce workload, and yet make the network systems be more vulnerable to various intrusion attacks. There has been realistic demand to develop an efficient intrusion detection algorithm for connected devices. Most of existing intrusion detection methods are trained in a centralized manner and are incapable to identify new unlabeled attack types. In this paper, a distributed federated intrusion detection method is proposed, utilizing the information contained in the labeled data as the prior knowledge to discover new unlabeled attack types. Besides, the blockchain technique is introduced in the federated learning process for the consensus of the entire framework. Experimental results are provided to show that our approach can identify the malicious entities, while outperforming the existing methods in discovering new intrusion attack types.

Graphical abstract

Keywords

intrusion detection / federated learning / new attacks discovering / blockchain

Nan Sun received BS degree in Detection Guidance and Control from Northwestern Polytechnical University, China in 2018 and MS degree in Aeronautical and Astronautical Science and Technologyfrom Harbin Institute of Technology, China in 2020, respectively. He is currently pursuing the PhD degree with the School of Cyber Science and Technology, Beihang University, China. His research interests include attack detection and federated learning

Wei Wang received her BEng degree in Electrical Engineering and Automation from Beihang University, China in 2005, MSc degree in Radio Frequency Communication Systems with Distinction from University of Southampton, UK in 2006 and PhD degree from Nanyang Technological University, Singapore in 2011. From January 2012 to June 2015, she was a lecturer with the Department of Automation at Tsinghua University, China. Currently, she is an associate professor with the School of Automation Science and Electrical Engineering at Beihang University and supported by BUAA Young Talent Recruitment Program. Her research interests include adaptive control of uncertain systems, distributed cooperative control of multi-agent systems, fault tolerant control, secure control of cyber-physical systems, and flight control systems

Yongxin Tong received the PhD degree in Computer Science and Engineering from The University of Hong Kong of Science and Technology, China in 2014. He is currently a professor in the School of Computer Science and Engineering, Beihang University, China. His research interests include federated learning, privacy preserving data analytics, big spatio-temporal data analytics, crowdsourcing and reinforcement learning. He published more than 100 papers in prestigious international journals (e.g., ACM TODS, IEEE TKDE, and VLDBJ) and conferences (e.g., SIGMOD, SIGKDD, VLDB, and ICDE). He is an associate editor for IEEE Transactions on Knowledge and Data Engineering (TKDE), etc. He received Alibaba DAMO Academy Young Fellow in 2018, the Excellent Demonstration Award from VLDB 2014, the champion from KDD Cup 2020

Kexin Liu received the MSc degree in Control Science and Engineering from Shandong University, China in 2013, and PhD degree in System Theory from Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China in 2016, respectively. From 2016 to 2018, he was a postdoctoral fellow in Peking University, China. Currently, he is an associate professor with the School of Automation Science and Electrical Engineering, Beihang University, China. His research interests include multi-agent systems and complex networks

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Acknowledgements

This work was supported in part by the National Key R&D Program of China (2018AAA0101100), and in part by the National Natural Science Foundation of China (Grant Nos. 62022008 and 92067204).

Competing interests

The authors declare that they have no competing interests or financial conflicts to disclose.

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2024 Higher Education Press