Reducing the Effects of Deception Attack on GPS Receivers of Phasor Measurement Units using Neural Networks

Document Type : Original Article

Authors

1 Master's student, Faculty of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

2 Ph.D., Faculty of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

3 Professor, Faculty of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

Accurate timing is one of the key features of the Global Positioning System (GPS), which is employed in many critical infrastructures. Any imprecise time measurement in GPS-based structures, such as smart power grids, and Phasor Measurement Units (PMUs), can lead to disastrous results. The vulnerability of the stationary GPS receivers to the Time Synchronization Attacks (TSAs) jeopardizes the GPS timing precision and trust level. In this paper, the PMU receiver clock deviation monitoring method is used. In this method, a deception and fraud reduction algorithm is presented based on clock deviation observations. A multi-layer perceptron neural network is trained to track clock behavior information behavior and maintain a valid trend under time-synchronization attack conditions that can dramatically mimic clock deviation. Finally, the results were compared with a strong and low-memory RE estimator, which is one of the most recent methods of counteracting TSA, as well as an extended Kalman filter and a Luenberger observer. This indicates the good performance of the proposed method.

Keywords

Smiley face

References

[1]    P. P. Barker and R. W. De Mello, “Determining the Impact of Distributed Generation on Power Systems: Part 1-Radial Distribution Systems,” Proc. IEEE PES Summer Power Meeting, Seattle, WA, pp. 1645-1656, Jul. 2000.
[2]    S. Silva, T. Hagan, J. Kim, E. Cotilla-Sanchez et al., “Sparse Error Correction for PMU Data under GPS Spooning Attack,” IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 902-906, 2018.
[3]    IEEE Standard for Synchrophasors for Power Systems, IEEE Std. C37.118-2005 (Revision of IEEE Std. 1344-1995), 2006.
[4]    M. Mosavi, S. Tohidi, M. Moazedi, “Reduce the Effect of Interference on the GPS Receiver by using Multiple Correlators,” Journal of Electronic and Cyber Defense, vol. 9, no. 3, pp. 49-57, December 2021, (In Persian).
[5]    F. Diggelen, P. Enge, “The world’s First GPS MOOC and Worldwide Laboratory using Smartphones,” In Proceedings of the 28th International Technical Meeting of the Satellite Division of the Institute of Navigation” (ION GNSS+ 2015), pp. 361-369, 2015.
[6]    W. Lewandowski, G. Petit, C. Thomas “Precision and Accuracy of GPS Time Transfer,” IEEE Tra Instrum Meas, vol. 42, no. 2, pp. 474-479, 1993.
[7]    M. Cui, J. Wang, and M. Yue, “Machine Learning Based Anomaly Detection for Load Forecasting under Cyberattacks,” IEEE Trans. Smart Grid, vol. 10, no. 5, pp. 5724-5734, Sep 2019.
[8]    J. S. Warner and R. G. Johnston, “A Simple Demonstration That the Global Positioning System (GPS) is Vulnerable to Spoofing,” Journal of Security Administration, vol. 25, pp.19-28, 2003.
[9]    X. Fan, S. Pal, D. Duan and L. Du, “Closed-Form Solution for Synchro Phasor Data Correction under GPS Spoofing Attack,” IEEE Power & Energy Society General Meeting (PESGM), pp.1-5, 2018.
[10] M. Mosavi, M. Moazedi, M. Rezaee, A. Tabatabaei, “Dealing with Disturbances in GPS Receivers,” Iran University of Science and Technology Publications, 2015.
[11] D. Schmidt, K. Radke, S. Camtepe, E. Foo, M. Ren, “A Survey and Analysis of the GNSS Spoofing Threat and Countermeasures,” ACM Comput Surv, vol. 48, no. 4, pp. 1-31, 2016.
[12] E. Schmidt, J. Lee, N. Gatsis, D. Akopian, “Rejection of Smooth GPS Time Synchronization Attacks via Sparse Techniques,” IEEE Sens J, vol. 21, no. 1, pp. 776-789, 2020.
[13] X. Jiang, J. Zhang, B. J. Harding, J. J. Makela, A. D. Dominguez-Garcia, “Spoofing GPS Receiver Clock Offset of phasor Measurement Units,” IEEE Trans Power Syst, vol. 28, no. 3, pp. 3253-3262, 2013.
[14] J. Magiera, “A Multi-Antenna Scheme for Early Detection and Mitigation of Intermediate GNSS Spoofing,” Sensors, vol. 19, no. 10, 2019.
[15] K. Ghorbani, N. Orouji, M. R. Mosavi, “Navigation Message Authentication Based on one-way Hash Chain to Mitigate Spoofing Attacks for GPS L1,” Wireless Pers Commun, vol. 113, no. 4, pp. 1743-1754, 2020.
[16] M. L. Psiaki, B. W. O’Hanlon, J. A. Bhatti, D. P. Shepard, T. E. Humphreys, “GPS Spoofing Detection via Dual-Receiver Correlation of Military Signals,” IEEE Trans Aerosp Electron Syst, vol. 49, no. 4, pp. 2250-2267, 2013.
[17] M. R. Mosavi, A. Tabatabaei, M. J. Zandi, “Positioning Improvement by Combining GPS and GLONASS Based on Kalman Filter and Its Application in GPS Spoofing Situations,” Gyroscop Navig, vol. 7, no. 4, pp. 318-325, 2016.
[18] N. Orouji and M. R. Mosavi, “A Multi-Layer Perceptron Neural Network to Mitigate the Interference of Time Synchronization Attacks in Stationary GPS Receivers,” GPS Solut, vol. 25, no. 3, Jul. 2021.
[19] J. A. Volpe, “Vulnerability Assessment of the Transportation Infrastructure Relying on the Global Positioning System,” 2001.
[20] M. R. Mosavi, F. Shafiee, “Narrowband Interference Suppression for GPS Navigation using Neural Networks,” GPS Solutions, vol. 20, no. 3, pp. 341-351, 2016.
[21] F. A. Ibrahim, “Optimal Linear Neuron Learning and Kalman Filter Based Backpropagation Neural Network for DGPS/INS Integration,” IEEE Conference on Position, Location and Navigation Symposium, pp. 1175-1189, 2008.
[22]   J. Lee, A. F. Taha, N. Gatsis, D. Akopian, “Tuning-free, Low Memory Robust Estimator to Mitigate GPS Spoofing Attacks,” IEEE Control Systems Letters, vol. 4, no. 1, pp. 145-150, 2019.
Electronic and Cyber Defense
Volume 11, Issue 1 - Serial Number 41
No. 41, Spring
May 2023
Pages 97-105

Files

History

  • Receive Date: 12 April 2022
  • Revise Date: 22 June 2022
  • Accept Date: 24 December 2022
  • Publish Date: 22 May 2023

Share

How to cite

Statistics