Fan, Y. orcid.org/0000-0001-8038-7337, Zhang, L. orcid.org/0000-0002-4535-3200 and Li, K. (2024) AE-BiLSTM: Multivariate Time-Series EMI Anomaly Detection in 5G-R High-Speed Rail Wireless Communications. In: 2024 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE ICC 2024 Workshop On Machine Learning And Deep Learning For Wireless Security, 09 Jun 2024, Denver, CO, USA. IEEE , pp. 439-444. ISBN 979-8-3503-0406-0
Abstract
With the global expansion of high-speed rail (HSR) and the integration of the latest wireless communication networks into the railway system, establishing a secure bidirectional communication link between moving trains and base stations (BSs) is vital to ensure real-time control. The increasing complexity of contemporary railway systems and heightened exposure to electromagnetic interference (EMI) have led to operational disruptions and security risks. This paper introduces a real-time anomaly detection approach that utilizes a deep learning algorithm based on autoencoder (AE) and long short-term memory (LSTM). By analyzing multivariate time series characteristics, the method simultaneously examines the time and frequency domains at a finer resolution, achieving a desirable trade-off between false alarms and missed anomalies. Specifically, our approach enhances accuracy by 5%, reaching 93.24% in comparison with some state-of-the-art methods. The online detection takes 4.51 ms, meeting the security latency requirements. This highlights the potential for timely detection of unforeseen EMI incidents in diverse scenarios and at varying speeds.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Anomaly Detection, Electromagnetic Interference, Deep Learning, High-Speed Rail Wireless Communications |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Apr 2024 14:20 |
Last Modified: | 13 Sep 2024 14:54 |
Published Version: | https://ieeexplore.ieee.org/document/10615719 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ICCWorkshops59551.2024.10615719 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:211190 |