Abstract
Due to its wide-area and high-precision advantages, Global Navigation Satellite System (GNSS) timing is widely employed in critical infrastructures such as power, communication and transportation, maintaining high-precision time synchronization for the system. Nevertheless, due to the lack of authentication and unencrypted structure of civilian GNSS signals, GNSS receiver is vulnerable to be attacked, resulting in disastrous consequences. Therefore, detecting and mitigating a time synchronization attack (TSA) to improve the security of GNSS timing and ensure the normal operation of critical infrastructures is of great significance. We proposed a TSA detection and mitigation algorithm based on long short-term memory (LSTM) neural network. Based on the good nonlinear mapping ability and high self-learning ability of LSTM, the authentic trend of the receiver clock can be learned and clock state can be predicted. Based on the difference between the predicted and measured clock state of the receiver, TSA detection and mitigation can be realized. Experiments and results show that the proposed algorithm can detect and mitigate two well-known types of TSA. In Type I TSA case, the root-mean-square error (RMSE) is improved by 56.41, 89.14 and 0.01 compared with Robust Estimator (RE), Time Synchronization Attack Rejection and Mitigation (TSARM) method and Multi-Layer Perceptron (MLP) neural network, respectively. In Type II TSA case, the RMSE is improved by 41.80, 88.16 and 0.33 compared with RE, TSARM and MLP, respectively. The research results can be applied to time synchronization systems of critical infrastructures, which can improve time synchronization accuracy and security performance.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The GNSS datasets can be provided to readers by contacting the corresponding author on reasonable request.
References
Borio D, Gioia C (2021) Interference mitigation: impact on GNSS timing. GPS Solut. https://doi.org/10.1007/s10291-020-01075-x
Chauhan SVS, Gao GX (2021a) Synchrophasor data under Gps spoofing: attack detection and mitigation using residuals. IEEE Trans Smart Grid 12(4):3415–3424
Chauhan SVS, Gao GX (2021b) Spoofing resilient state estimation for the power grid using an extended kalman filter. IEEE Trans Smart Grid 12(4):3404–3414
Gao Y, Li G (2022) Three time spoofing algorithms for GNSS timing receivers and performance evaluation. GPS Solut. https://doi.org/10.1007/s10291-022-01275-7
Google (2020) Raw GNSS measurements. https://developer.android.google.cn/guide/topics/sensors/gnss.
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Huang B, Ji Z, Zhai R, Xiao C, Yang F (2021) Clock bias prediction algorithm for navigation satellites based on a supervised learning long short-term memory neural network. GPS Solut. https://doi.org/10.1007/s10291-021-01115-0
Jaduszliwer B, Camparo J (2021) Past, present and future of atomic clocks for GNSS. GPS Solut. https://doi.org/10.1007/s10291-020-01059-x
Jiang X, Zhang J, Harding BJ, Makela JJ, Dominguez-Garc´ıa A D, (2013) Spoofing GPS receiver clock offset of phasor measurement units. IEEE Trans Power Syst 28(3):3253–3262
Khalajmehrabadi A, Gatsis N, Akopian D, Taha AF (2018) Realtime rejection and mitigation of TSA on the global positioning system. IEEE Trans Ind Electron 65(8):6425–6435
Lee J, Taha AF, Gatsis N, Akopian D (2020) Tuning-free, low memory robust estimator to mitigate GPS spoofing attacks. IEEE Contr Syst Lett 4(1):145–150
Liang G, Zhao J, Luo F, Weller SR, Dong Z (2017) A review of false data injection attacks against modern power systems. IEEE Trans Smart Grid 8(4):1630–1638
Luenberger D (1966) Observers for multivariable systems. IEEE Trans Autom Control 11(2):190–197
Ma J, Liu H, Peng C, Qiu T (2020) Unauthorized broadcasting identification: a deep LSTM recurrent learning approach. IEEE Trans Instrum Meas 69(9):5981–5983
Matsakis, D (2007) The timing group delay (TGD) correction and GPS timing biases. In: Proceedings of the 63rd Annual Meeting of The Institute of Navigation pp 49–54
Mosavi MR, Tabatabaei A, Zandi MJ (2016) Positioning improvement by combining GPS and GLONASS based on Kalman filter and its application in GPS spoofing situations. Gyroscop Navig 7(4):318–325
Orouji N, Mosavi MR (2021) A multi-layer perceptron neural network to mitigate the interference of time synchronization attacks in stationary GPS receivers. GPS Solut. https://doi.org/10.1007/s10291-021-01124-z
Risbud P, Gatsis N, Taha A (2019) Vulnerability analysis of smart grids to GPS spoofing. IEEE Trans Smart Grid 10(4):3535–3548
Schmidt E, Lee J, Gatsis N, Akopian D (2021) Rejection of smooth GPS time synchronization attacks via sparse techniques. IEEE Sensors J 21(1):776–789
Shereen E, Delcourt M, Barreto S, Dan G, Boudec J-YL, Paolone M (2020) Feasibility of time-synchronization attacks against PMU based state estimation. IEEE Trans Instrum Meas 69(6):3412–3427
Wang Y (2018) Distributed estimation of power system oscillation modes under attacks on GPS clocks. IEEE Trans Instrum Meas 67(7):1626–1637
Wang C, Kong L, Jiang J, Lai Y (2021) Machine learning-based approach to GPS antijamming. GPS Solut. https://doi.org/10.1007/s10291-021-01154-7
Wang Y, Kou Y, Zhao Y, Huang Z (2022) Detection of synchronous spoofing on a GNSS receiver using weighed double ratio metrics. GPS Solut. https://doi.org/10.1007/s10291-022-01268-6
Yao J, Yoon S, Stressler B, Hilla S, Schenewerk M (2021) GPS satellite clock estimation using global atomic clock network. GPS Solut. https://doi.org/10.1007/s10291-021-01145-8
Yao J, Weiss M, Curry C, Levine J (2016) GPS Jamming and GPS Carrier-Phase Time Transfer. In: Proceedings of the 47th Annual Precise Time and Time Interval Systems and Applications Meeting, pp 80–85
Acknowledgements
Not applicable.
Funding
This work was supported by the National Key Research and Development Program of China (Grant No. 2021YFA0716500), the National Natural Science Foundation of China (Grant Nos. 61973328, 91938301) and Shenzhen Science and Technology Plan Project (Grant No. ZDSYS20210623091807023).
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. XZ and BX presented the basic idea of the paper and revised the paper. YL performed the experiment and wrote the paper. ZC, DS and ZZ contributed to the discussion about the content and provided comments on the manuscript. All authors have read and agreed to the published version of the manuscript.
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Ethical approval and consent to participate
Not applicable.
Consent for publication
All authors read and approved the final manuscript to be published.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Liu, Y., Xu, B., Chen, Z. et al. Detection and mitigation of time synchronization attacks based on long short-term memory neural network. GPS Solut 28, 46 (2024). https://doi.org/10.1007/s10291-023-01587-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10291-023-01587-2