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Volume 13 Issue 8
Aug.  2026

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2026  >  13(8): 1883-1903
S.-Q. Wang, J. Liu, B.-G. Cai, J. Wang, D.-B. Lu, and W. Jiang, “Brain-inspired GNSS spoofing attack cognition using spiking neural network for train positioning,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 8, pp. 1883–1903, Aug. 2026. doi: 10.1109/JAS.2025.125903
Citation: S.-Q. Wang, J. Liu, B.-G. Cai, J. Wang, D.-B. Lu, and W. Jiang, “Brain-inspired GNSS spoofing attack cognition using spiking neural network for train positioning,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 8, pp. 1883–1903, Aug. 2026. doi: 10.1109/JAS.2025.125903
shu

Brain-Inspired GNSS Spoofing Attack Cognition Using Spiking Neural Network for Train Positioning

doi: 10.1109/JAS.2025.125903
Funds:  This work was supported by the National Natural Science Foundation of China (U2268206, T2422002, T2222015), State Key Laboratory of Advanced Rail Autonomous Operation (RAO2025ZD001), Beijing Jiaotong University, and Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China (JYB2025XDXM211)
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    Abstract
  • Global Navigation Satellite System (GNSS) has been an exciting growing research field in railway transportation due to its accuracy, low cost, and global availability. However, GNSS-based train positioning is susceptible to spoofing attacks in complex railway operation environments due to GNSS vulnerabilities. Aiming at resilience assurance of train positioning using GNSS, this paper proposes a spoofing attack cognition solution based on brain-inspired intelligence. Specifically, a multi-domain joint Spoofing Detector (SD) is proposed based on the Spiking Neural Network (SNN) to perceive the cybersecurity threat from spoofing attacks. Furthermore, the active spoofing protection solution is designed using a brain-inspired cognition approach considering different spoofing attack situation phases. An Attack Situation Recognizer (ASR) network combining signal decomposition and identification is proposed, which features SNN-optimized residual blocks and the cross-attention fusion modules, leading to enhanced recognition capability at a low computational cost level. Experiments based on field data and a GNSS spoofing injection testbench platform demonstrate the performance of this solution in spoofing attack identification and situation cognition over different referencing methods. The results illustrate that the proposed solution aligns with the railway Positioning, Navigation & Timing (PNT) resilience requirement by leveraging the advantages of brain-like intelligence in enhancing the trustworthiness of GNSS positioning in railway transportation systems.

     

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  • [1]
    I. Butun, P. Österberg, and H. Song, “Security of the Internet of Things: Vulnerabilities, attacks, and countermeasures,” IEEE Commun. Surv. Tut., vol. 22, no. 1, pp. 616–644, Jan.–Mar. 2020. doi: 10.1109/COMST.2019.2953364
    [2]
    Y. Du, J. Wang, C. Rizos, and A. El-Mowafy, “Vulnerabilities and integrity of precise point positioning for intelligent transport systems: Overview and analysis,” Satell. Navig., vol. 2, no. 1, Art. no. 3, Feb. 2021.
    [3]
    Z. Zhang, H. Qin, D. Huang, X. Fang, M. Zhou, and S. Guo, “From singleton to collaboration: Robust 3D cooperative positioning for intelligent connected vehicles based on hybrid range-azimuth-elevation under zero-trust driving environments,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 8, pp. 1568–1585, Aug. 2025. doi: 10.1109/JAS.2024.124698
    [4]
    J. Zhang, L. Pan, Q.-L. Han, C. Chen, S. Wen, and Y. Xiang, “Deep learning based attack detection for cyber-physical system cybersecurity: A survey,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 377–391, Mar. 2022. doi: 10.1109/JAS.2021.1004261
    [5]
    J. Pan, A. T. Nguyen, S. Wang, H. Deng, and H. Zhang, “Fuzzy unknown input observer for estimating sensor and actuator cyber-attacks in intelligent connected vehicles,” Automot. Innov., vol. 6, no. 2, pp. 164–175, Jun. 2023. doi: 10.1007/s42154-023-00228-1
    [6]
    R. Ehrler, A. Wenz, S. Baumann, P. Mendes, N. Dütsch, A. Martin, and C. Hinterstocker, “Jamming and spoofing impact on GNSS signals for railway applications,” in Proc. 36th Int. Technical Meeting of the Satellite Division of the Institute of Navigation, Denver, USA, 2023, pp. 4153–4167.
    [7]
    C. Sun, J. W. Cheong, A. G. Dempster, L. Demicheli, E. Cetin, H. Zhao, and W. Feng, “Moving variance-based signal quality monitoring method for spoofing detection,” GPS Solut., vol. 22, no. 3, Art. no. 83, Jun. 2018.
    [8]
    W. Zhou, Z. Lv, X. Deng, and Y. Ke, “A new induced GNSS spoofing detection method based on weighted second-order central moment,” IEEE Sens. J., vol. 22, no. 12, pp. 12064–12078, Jun. 2022. doi: 10.1109/JSEN.2022.3174019
    [9]
    X. Shang, F. Sun, B. Liu, L. Zhang, and J. Cui, “GNSS spoofing mitigation with a multicorrelator estimator in the tightly coupled INS/GNSS integration,” IEEE Trans. Instrum. Meas., vol. 72, Art. no. 8500112, 2023.
    [10]
    Z. Chen, J. Li, J. Li, X. Zhu, and C. Li, “GNSS multiparameter spoofing detection method based on support vector machine,” IEEE Sens. J., vol. 22, no. 18, pp. 17864–17874, Sep. 2022. doi: 10.1109/JSEN.2022.3193388
    [11]
    Z. Feng, N. Guan, M. Lv, W. Liu, Q. Deng, X. Liu, and W. Yi, “Efficient drone hijacking detection using two-step GA-XGBoost,” J. Syst. Archit., vol. 103, Art. no. 101694, Feb. 2020.
    [12]
    X. Geng, Y. Guo, K. Tang, W. Wu, Y. Ren, and G. Duan, “A covert spoofing algorithm for SINS/GNSS tightly integrated navigation system,” IEEE Trans. Autom. Sci. Eng., vol. 22, pp. 6134−6142, 2025.
    [13]
    E. Shafiee, M. R. Mosavi, and M. Moazedi, “Detection of spoofing attack using machine learning based on multi-layer neural network in single-frequency GPS receivers,” J. Navig., vol. 71, no. 1, pp. 169–188, Jan. 2018. doi: 10.1017/S0373463317000558
    [14]
    P. Borhani-Darian, H. Li, P. Wu, and P. Closas, “Deep neural network approach to detect GNSS spoofing attacks,” in Proc. 33rd Int. Technical Meeting of the Satellite Division of the Institute of Navigation, 2020, pp. 3241−3252.
    [15]
    A. Iqbal, M. N. Aman, and B. Sikdar, “A deep learning based induced GNSS spoof detection framework,” IEEE Trans. Mach. Learn. Commun. Netw., vol. 2, pp. 457–478, Apr. 2024. doi: 10.1109/TMLCN.2024.3386649
    [16]
    L. L. Maynard, “GNSS spoofing detection using machine learning and truncated singular value decomposition,” in Proc. 35th Int. Technical Meeting of the Satellite Division of the Institute of Navigation, Denver, USA, 2022, pp. 1137−1150.
    [17]
    S. Dasgupta, M. Rahman, M. Islam, and M. Chowdhury, “A sensor fusion-based GNSS spoofing attack detection framework for autonomous vehicles,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 12, pp. 23559–23572, Dec. 2022. doi: 10.1109/TITS.2022.3197817
    [18]
    J. Brewington and D. Kar, “UAV GPS spoofing detection via neural generative one-class classification,” in Proc. 24th Int. Symp. Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, Washington, USA, 2023, pp. 492−497.
    [19]
    Y. Dang, A. Karakoc, and R. Jäntti, “Graphic neural network based GPS spoofing detection for cellular-connected UAV swarm,” in Proc. 97th Vehicular Technology Conf., Florence, Italy, 2023, pp. 1−6.
    [20]
    W. Ou, S. Xiao, C. Zhu, W. Han, and Q. Zhang, “An overview of brain-like computing: Architecture, applications, and future trends,” Front. Neurorobot., vol. 16, Art. no. 1041108, Nov. 2022.
    [21]
    Y. Tian, Y. Li, K. Chen, E. Tong, W. Niu, J. Liu, F. Qin, and Z. Zheng, “Mitigating overfitting for deep learning-based aging-related bug prediction via brain-inspired regularization in spiking neural networks,” in Proc. 34th Int. Symp. Software Reliability Engineering Workshops, Florence, Italy, 2023, pp. 214−221.
    [22]
    M. Yao, J. Hu, T. Hu, Y. Xu, Z. Zhou, Y. Tian, B. Xu, and G. Li, “Spike-driven transformer V2: Meta spiking neural network architecture inspiring the design of next-generation neuromorphic chips,” in Proc. 12th Int. Conf. Learning Representations, Vienna, Austria, 2024.
    [23]
    S. Park, D. Lee, and S. Yoon, “Noise-robust deep spiking neural networks with temporal information,” in Proc. 58th ACM/IEEE Design Automation Conf., San Francisco, USA, 2021, pp. 373−378.
    [24]
    W. Fang, Z. Yu, Y. Chen, T. Masquelier, T. Huang, and Y. Tian, “Incorporating learnable membrane time constant to enhance learning of spiking neural networks,” in Proc. IEEE/CVF Int. Conf. Computer Vision, Montreal, Canada, 2021, pp. 2641−2651.
    [25]
    X. Yao, F. Li, Z. Mo, and J. Cheng, “GLIF: A unified gated leaky integrate-and-fire neuron for spiking neural networks,” in Proc. 36th Int. Conf. Neural Information Processing Systems, New Orleans, USA, 2022, Art. no. 2330.
    [26]
    X. Liang, Q. Wu, Y. Zhou, C. Tan, H. Yin, and C. Sun, “Spiking reinforcement learning enhanced by bioinspired event source of multi-dendrite spiking neuron and dynamic thresholds,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 618–629, Mar. 2025. doi: 10.1109/JAS.2024.124551
    [27]
    W. Fang, Z. Yu, Y. Chen, T. Huang, T. Masquelier, and Y. Tian, “Deep residual learning in spiking neural networks,” in Proc. 35th Int. Conf. Neural Information Processing Systems, 2021, Art. no. 1611.
    [28]
    Z. Zhou, Y. Zhu, C. He, Y. Wang, S. Yan, Y. Tian, and L. Yuan, “Spikformer: When spiking neural network meets transformer,” in Proc. 11th Int. Conf. Learning Representations, Kigali, Rwanda, 2023.
    [29]
    U. Rançon, J. Cuadrado-Anibarro, B. R. Cottereau, and T. Masquelier, “StereoSpike: Depth learning with a spiking neural network,” IEEE Access, vol. 10, pp. 127428–127439, Dec. 2022. doi: 10.1109/ACCESS.2022.3226484
    [30]
    Y. Yuan, Y. Wang, and X. Luo, “A node-collaboration-informed graph convolutional network for highly accurate representation to undirected weighted graph,” IEEE Trans. Neural Netw. Learn. Syst., vol. 36, no. 6, pp. 11507–11519, Jun. 2025. doi: 10.1109/TNNLS.2024.3514652
    [31]
    L. Wang, K. Liu, and Y. Yuan, “GT-A2T: Graph tensor alliance attention network,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 10, pp. 2165–2167, Oct. 2025. doi: 10.1109/JAS.2024.124863
    [32]
    Y. Yang, G. Li, D. Li, J. Zhang, P. Hu, and L. Hu, “Integrating fuzzy clustering and graph convolution network to accurately identify clusters from attributed graph,” IEEE Trans. Netw. Sci. Eng., vol. 12, no. 2, pp. 1112–1125, Mar.-Apr. 2025. doi: 10.1109/TNSE.2024.3524077
    [33]
    Y. Yang, L. Hu, G. Li, D. Li, P. Hu, and X. Luo, “Link-based attributed graph clustering via approximate generative Bayesian learning,” IEEE Trans. Syst., Man, Cybern.: Syst., vol. 55, no. 8, pp. 5730–5743, Aug. 2025. doi: 10.1109/TSMC.2025.3572738
    [34]
    Q. Jiang and X. Yan, “Multimode process monitoring using variational Bayesian inference and canonical correlation analysis,” IEEE Trans. Autom. Sci. Eng., vol. 16, no. 4, pp. 1814–1824, Oct. 2019. doi: 10.1109/TASE.2019.2897477
    [35]
    A. Pirsiavash, A. Broumandan, G. Lachapelle, and K. O’Keefe, “GNSS code multipath mitigation by cascading measurement monitoring techniques,” Sensors, vol. 18, no. 6, Art. no. 1967, Jun. 2018.
    [36]
    J. Peng, H. Shao, Y. Xiao, B. Cai, and B. Liu, “Industrial surface defect detection and localization using multi-scale information focusing and enhancement GANomaly,” Expert Syst. Appl., vol. 238, Art. no. 122361, Mar. 2024.
    [37]
    E. O. Neftci, H. Mostafa, and F. Zenke, “Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks,” IEEE Signal Process. Mag., vol. 36, no. 6, pp. 51–63, Nov. 2019. doi: 10.1109/MSP.2019.2931595
    [38]
    S. Akçay, A. Atapour-Abarghouei, and T. P. Breckon, “Skip-GANomaly: Skip connected and adversarially trained encoder-decoder anomaly detection,” in Proc. Int. Joint Conf. Neural Networks, Budapest, Hungary, 2019, pp. 1−8.
    [39]
    H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena, “Self-attention generative adversarial networks,” in Proc. 36th Int. Conf. Machine Learning, Long Beach, USA, 2019, pp. 7354−7363.
    [40]
    Y. Wang, Q. Liu, E. Mihankhah, C. Lv, and D. Wang, “Detection and isolation of sensor attacks for autonomous vehicles: Framework, algorithms, and validation,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 7, pp. 8247–8259, Jul. 2022. doi: 10.1109/TITS.2021.3077015
    [41]
    W. Zhou, H. T. Zhang, and J. Wang, “An efficient sparse Bayesian learning algorithm based on Gaussian-scale mixtures,” IEEE Trans. Neural Netw. Learn. Syst., vol. 33, no. 7, pp. 3065–3078, Jul. 2022. doi: 10.1109/TNNLS.2020.3049056
    [42]
    H. Zhang, C. Zhou, L. Yu, L. Huang, Z. Ma, X. Fan, H. Zhou, and Y. Tian, “SGLFormer: Spiking global-local-fusion transformer with high performance,” Front. Neurosci., vol. 18, Art. no. 1371290, Mar. 2024.
    [43]
    F. Lin, H. Yan, J. Li, Z. Liu, L. Lu, Z. Ba, and K. Ren, “PhaDe: Practical phantom spoofing attack detection for autonomous vehicles,” IEEE Trans. Inf. Forensics Secur., vol. 19, pp. 4199–4214, Mar. 2024. doi: 10.1109/TIFS.2024.3376192
    [44]
    C. Zhou, H. Zhang, Z. Zhou, L. Yu, L. Huang, X. Fan, L. Yuan, Z. Ma, H. Zhou, and Y. Tian, “QKFormer: Hierarchical spiking transformer using Q-K attention,” in Proc. 37th Int. Conf. Neural Information Processing Systems, Vancouver, Canada, 2024, Art. no. 416.
    [45]
    R.-J. Zhu, M. Zhang, Q. Zhao, H. Deng, Y. Duan, and L.-J. Deng, “TCJA-SNN: Temporal-channel joint attention for spiking neural networks,” IEEE Trans. Neural Netw. Learn. Syst., vol. 36, no. 3, pp. 5112–5125, Mar. 2025. doi: 10.1109/TNNLS.2024.3377717
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