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Machine Learning based Time Synchronization Attack Detection for Synchrophasors
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DOI：10.1109/GLOBECOM54140.2023.10437566.png)
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Abstract
En 中文
The reliable operation of phasor measurement units (PMU) in modern power grid monitoring system like wide-area measurement systems (WAMS) relies on accurate time synchronization, which is provided by the Global Positioning System (GPS). However, the open nature of civilian GPS signals makes PMUs vulnerable to time synchronization attacks (TSA), where attackers manipulate PMU time stamps by transmitting deceptive GPS signals near the PMUs. In this paper, we propose a framework for detecting TSA on PMUs using machine learning (ML) methods. We evaluate five ML algorithms, including Support Vector Machines, Random Forest, K-Nearest Neighbors, Gradient Boost, and Artificial Neural Network, and select seven complementary features that can be computed at the radio frequency (RF) and tracking stages of any commercial GPS receiver. Our detection protocol stands out from other similar ML-based methods in terms of speed, as it does not rely on waiting for the PVT solution. The Texas Spoofing Test Battery (TEXBAT) dataset is used to evaluate the proposed framework. We demonstrate that the ML models can effectively detect GPS spoofing with up to 99.9% probability while maintaining less than 0.5% false alarm and mis-detection probabilities. By providing early detection of GPS spoofing attacks on PMUs, the proposed framework has the potential to enhance the cybersecurity of WAMS.
Keywords:
GPS spoofing attacks
machine learning
PMU
smart grid
spoofing detection technique
TSA
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