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Tavasoli, A., Orouji, N., Mosavi, M. (2023). Reducing the Effects of Deception Attack on GPS Receivers of Phasor Measurement Units using Neural Networks. Electronic and Cyber Defense, 11(1), 97-105.
Afsaneh Tavasoli; Niloofar Orouji; Mohammad Reza Mosavi. "Reducing the Effects of Deception Attack on GPS Receivers of Phasor Measurement Units using Neural Networks". Electronic and Cyber Defense, 11, 1, 2023, 97-105.
Tavasoli, A., Orouji, N., Mosavi, M. (2023). 'Reducing the Effects of Deception Attack on GPS Receivers of Phasor Measurement Units using Neural Networks', Electronic and Cyber Defense, 11(1), pp. 97-105.
Tavasoli, A., Orouji, N., Mosavi, M. Reducing the Effects of Deception Attack on GPS Receivers of Phasor Measurement Units using Neural Networks. Electronic and Cyber Defense, 2023; 11(1): 97-105.

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

پدافند الکترونیکی و سایبری
Volume 11, Issue 1 - Serial Number 41, May 2023, Pages 97-105    PDF (1.39 M)
Document Type: Original Article
Authors
Afsaneh Tavasoli1; Niloofar Orouji2; Mohammad Reza Mosavi* 3
1Master's student, Faculty of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
2Ph.D., Faculty of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
3Professor, Faculty of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
Receive Date: 12 April 2022,  Revise Date: 22 June 2022,  Accept Date: 24 December 2022 
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
Phasor Mesurement Units; Spoofing Attacks; Time Synchronization Attacks; Neural Network; Global Positioning System
References

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