Volume 32 Issue 5
Sep.  2023
Turn off MathJax
Article Contents
YANG Li, WEI Xiukun, WEN Chenglin, “A Security Defense Method Against Eavesdroppers in the Communication-Based Train Control System,” Chinese Journal of Electronics, vol. 32, no. 5, pp. 992-1001, 2023, doi: 10.23919/cje.2022.00.248
Citation: YANG Li, WEI Xiukun, WEN Chenglin, “A Security Defense Method Against Eavesdroppers in the Communication-Based Train Control System,” Chinese Journal of Electronics, vol. 32, no. 5, pp. 992-1001, 2023, doi: 10.23919/cje.2022.00.248

A Security Defense Method Against Eavesdroppers in the Communication-Based Train Control System

doi: 10.23919/cje.2022.00.248
Funds:  This work was supported by the National Natural Science Foundation of China (62120106011, 61933013, 61733015, U1934221) and the State Key Laboratory of Rail Traffic Control and Safety Contract, Beijing Jiaotong University (RCS2021K007).
More Information
  • Author Bio:

    Li YANG was born in 1992. He received the M.E. degree in electronics and communication engineering from the Guangxi Normal University in 2018. He is currently working toward the Ph.D. degree in Cyberspace College of Hangzhou Dianzi University, Hangzhou, China. His main research interests include cyber-physical system security defense and privacy protection, cyber-physical system attack methods. (Email: yl_hhgz@163.com)

    Xiukun WEI received the Ph.D. degree from Johannes Kepler University, Linz, Austria, in 2002. From 2006 to 2009, he was a Postdoctoral Researcher with the Delft Center for System and Control, Delft University of Technology, Delft, The Netherlands. From 2002 to 2006, he was a Research Assistant with the Institute of Design and Control of Mechatronical Systems, Johannes Kepler University. He is currently a Professor with the State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, China. His research interests include fault diagnosis and its applications, intelligent transportation systems, and condition monitoring and its applications in a variety of fields, such as rail traffic control, safety, and transportation. (Email: xkwei@bjtu.edu.cn)

    Chenglin WEN (corresponding author) was born in 1963. He graduated from Henan University in 1986, graduated from Zhengzhou University with a master degree in 1993 and received a Ph.D. degree from Northwestern Polytechnical University in 1999. He went out of the postdoctoral mobile station of control science and engineering of Tsinghua University in 2002. He is a Professor of Hangzhou Dianzi University and Guangdong University of Petrochemical Technology. His research interests include information fusion and target detection, fault diagnosis and active security control, deep learning and optimization decision-making systems, cyberspace security and attack detection and positioning. (Email: wencl@hdu.edu.cn)

  • Received Date: 2022-07-30
  • Accepted Date: 2022-09-26
  • Available Online: 2023-01-18
  • Publish Date: 2023-09-05
  • The communication-based train control system is the safety guarantee for automatic train driving. Wireless communication brings network security risks to the communication-based train control system. The eavesdropping of the transmitted information by unauthorized third-party personnel will lead to the leakage of the estimated value of the system state, which will lead to major accidents. This paper focuses on solving the problem of defense against eavesdropping threats and proposes an eavesdropping defense architecture. This defense architecture includes a coding mechanism based on punishing eavesdroppers, an information upload trigger mechanism based on contribution, and a random information transmission strategy, and provides a guarantee for the privacy protection of information. This research makes three contributions. First, it is the first attempt to construct an information encoding mechanism with punishing eavesdroppers as the objective function; Second, for the first time, an information upload trigger mechanism based on contribution is proposed; Third, the strategy of random transmission of information is proposed. The proposed method in this paper is verified by taking the medium and low-speed maglev train as the object. The experimental results show that, compared with Gaussian noise and non-Gaussian noise mechanisms, the coding mechanism proposed in this paper can not only protect the security of information but also make the estimation error of eavesdroppers tend to be infinite. Using the state estimation error as a metric, the average growth rate of the state estimation error of the system using the trigger mechanism in this paper is less than 2% while improving the security of the system. The transmission strategy in this paper does not increase the system state estimation error while improving the security of the system.
  • loading
  • [1]
    Z. X. Deng, H. F. Song, H. Huang, et al., “Multi-sensor based train localization and data fusion in autonomous train control system,” in Proceedings of 2020 Chinese Automation Congress (CAC), Shanghai, China, 2020.
    [2]
    T. Wen, G. Xie, Y. Cao, et al., “A DNN-based channel model for network planning in train control systems,” IEEE Transactions on Intelligent Transportation Systems, vol.23, no.3, pp.2392–2399, 2022. doi: 10.1109/TITS.2021.3093025
    [3]
    L. Yang, C. L. Wen, and T. Wen, “Multilevel fine fingerprint authentication method for key operating equipment identification in cyber-physical systems,” IEEE Transactions on Industrial Informatics, vol.19, no.2, pp.1217–1226, 2023. doi: 10.1109/TII.2022.3193955
    [4]
    S. Kim, Y. Won, I. H. Park, et al., “Cyber-physical vulnerability analysis of communication-based train control,” IEEE Internet of Things Journal, vol.6, no.4, pp.6353–6362, 2019. doi: 10.1109/JIOT.2019.2919066
    [5]
    H. W. Wang, F. R. Yu, L. Zhu, et al., “A cognitive control approach to communication-based train control systems,” IEEE Transactions on Intelligent Transportation Systems, vol.16, no.4, pp.1676–1689, 2015. doi: 10.1109/TITS.2014.2377115
    [6]
    X. Wang, L. Zhu, H. W. Wang, et al., “Robust distributed cruise control of multiple high-speed trains based on disturbance observer,” IEEE Transactions on Intelligent Transportation Systems, vol.22, no.1, pp.267–279, 2021. doi: 10.1109/TITS.2019.2956162
    [7]
    L. Zhu, Y. Li, F. R. Yu, et al., “Cross-layer defense methods for jamming-resistant CBTC systems,” IEEE Transactions on Intelligent Transportation Systems, vol.22, no.11, pp.7266–7278, 2021. doi: 10.1109/TITS.2020.3005931
    [8]
    L. Wang, X. H. Cao, B. W. Sun, et al., “Optimal schedule of secure transmissions for remote state estimation against eavesdropping,” IEEE Transactions on Industrial Informatics, vol.17, no.3, pp.1987–1997, 2021. doi: 10.1109/TII.2020.2995385
    [9]
    B. Gao and B. Bu, “Deep reinforcement learning-based resilient control method for CBTC systems through train-to-train communications under adversarial attacks,” in Proceedings of 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA, pp.3679–3684, 2021.
    [10]
    S. M. Ma, B. Bu, and H. W. Wang, “A virtual coupling approach based on event-triggering control for CBTC systems under jamming attacks,” in Proceedings of the IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), Victoria, BC, Canada, pp.1–6, 2020.
    [11]
    T. Wen, L. Zou, J. L. Liang, et al., “Recursive filtering for communication-based train control systems with packet dropouts,” Neurocomputing, vol.275, pp.948–957, 2018. doi: 10.1016/j.neucom.2017.09.038
    [12]
    L. Zou, T. Wen, Z. D. Wang, et al., “State estimation for communication-based train control systems with CSMA protocol,” IEEE Transactions on Intelligent Transportation Systems, vol.20, no.3, pp.843–854, 2019. doi: 10.1109/TITS.2018.2835655
    [13]
    J. H. Huang, D. W. C. Ho, F. F. Li, et al., “Secure remote state estimation against linear man-in-the-middle attacks using watermarking,” Automatica, vol.121, article no.109182, 2020. doi: 10.1016/j.automatica.2020.109182
    [14]
    H. Guo, Z. H. Pang, J. Sun, et al., “An output-coding-based detection scheme against replay attacks in cyber-physical systems,” IEEE Transactions on Circuits and Systems Ⅱ: Express Briefs, vol.68, no.10, pp.3306–3310, 2021. doi: 10.1109/TCSII.2021.3063835
    [15]
    W. Yang, D. K. Li, H. Zhang, et al., “An encoding mechanism for secrecy of remote state estimation,” Automatica, vol.120, article no.109116, 2020. doi: 10.1016/j.automatica.2020.109116
    [16]
    X. Y. Wang, J. C. Wang, X. Ma, et al., “A differential privacy strategy based on local features of non-Gaussian noise in federated learning,” Sensors, vol.22, no.7, article no.2424, 2022. doi: 10.3390/s22072424
    [17]
    B. Jia, X. S. Zhang, J. W. Liu, et al., “Blockchain-enabled federated learning data protection aggregation scheme with differential privacy and homomorphic encryption in ⅡoT,” IEEE Transactions on Industrial Informatics, vol.18, no.6, pp.4049–4058, 2022. doi: 10.1109/TII.2021.3085960
    [18]
    L. Yang and C. L. Wen, “Optimal jamming attack system against remote state estimation in Wireless network control systems,” IEEE Access, vol.9, pp.51679–51688, 2021. doi: 10.1109/ACCESS.2020.3046483
    [19]
    Y. E. Sun, H. Huang, W. J. Yang, et al., “Toward differential privacy for traffic measurement in vehicular cyber-physical systems,” IEEE Transactions on Industrial Informatics, vol.18, no.6, pp.4078–4087, 2022. doi: 10.1109/TII.2021.3120232
    [20]
    B. Jiang, J. Q. Li, G. H. Yue, et al., “Differential privacy for industrial internet of things: Opportunities, applications, and challenges,” IEEE Internet of Things Journal, vol.8, no.13, pp.10430–10451, 2021. doi: 10.1109/JIOT.2021.3057419
    [21]
    L. H. Peng, X. H. Cao, C. Y. Sun, et al., “Energy efficient jamming attack schedule against remote state estimation in wireless cyber-physical systems,” Neurocomputing, vol.272, pp.571–583, 2018. doi: 10.1016/j.neucom.2017.07.036
    [22]
    C. Y. Li, J. N. Wang, J. Y. Shan, et al., “Robust cooperative control of networked train platoons: A negative-imaginary systems’ perspective,” IEEE Transactions on Control of Network Systems, vol.8, no.4, pp.1743–1753, 2021. doi: 10.1109/TCNS.2021.3084064
    [23]
    H. F. Song, S. G. Gao, Y. D. Li, et al., “Train-centric communication based autonomous train control system,” IEEE Transactions on Intelligent Vehicles, vol.8, no.1, pp.721–731, 2023. doi: 10.1109/TIV.2022.3192476
    [24]
    Q. Dong, K. Hayashi, and M. Kaneko, “A new adaptive modulation and coding method for communication-based train control systems using WLAN,” IFAC-PapersOnLine, vol.49, no.22, pp.139–144, 2016. doi: 10.1016/j.ifacol.2016.10.386
    [25]
    X. H. Sun, C. L. Wen, and T. Wen, “Maximum correntropy high-order extended Kalman filter,” Chinese Journal of Electronics, vol.31, no.1, pp.190–198, 2022. doi: 10.1049/cje.2020.00.334
    [26]
    M. X. Zhang, “Study on operation control system of medium-low speed maglev train,” Master’s Thesis, Southwest Jiaotong University, Chengdu, China, 2019. (in Chinese)
    [27]
    L. F. Liu, Z. Y. Xi, et al., “Noise-based-protection message dissemination method for insecure opportunistic underwater sensor networks,” IEEE Transactions on Information Forensics and Security, vol.17, pp.1610–1623, 2022. doi: 10.1109/TIFS.2022.3167911
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(9)  / Tables(4)

    Article Metrics

    Article views (416) PDF downloads(36) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return