This paper proposes a machine learning framework to detect time synchronization attacks on PMUs, using ML algorithms and RF/tracking features. It achieves 99.9 ...

This paper proposed a novel framework for detecting time synchronization attacks on synchrophasors by utilizing seven features that can be readily computed at ...

Dec 13, 2023 · 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 ...

In this paper, we propose a framework for TSA detection using machine learning (ML) models at the control center of a WAMS. The feature set used includes power ...

This paper proposes a framework for detecting time synchronization attacks on PMUs using machine learning, using a spoof detector based on representation ...

May 10, 2022 · In this testbed we conduct FCI and FDI attacks on real-time C37.118 data packets using a packet manipulation tool called Scapy. Using this.

Nov 24, 2023 · Time Synchronization Attacks (TSAs) pose a significant threat to the precise functioning of phasor measurement units (PMUs) in the context of smart grids.

The main idea is to apply machine learning techniques on the historical data to build a general model for the normal operations of the WAMS and then conduct ...

The paper proposes a framework using STFT and YOLOv3 for synchrophasor data spoofing detection, using time-frequency feature extraction and object detection.

Jul 6, 2025 · The mechanism uses Bi-GRU classifiers to detect and classify intrusions (TSA/RA), then Bessel interpolation to filter and replace spoofed data.