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This paper presents an edge-based detection and localization approach for adversarial oscillatory load attacks initiated by compromised electric vehicle charging stations (EVCSs) against the power grid. The proposed deep learning mechanism achieves a classification accuracy of 99.4% by analyzing real-time data from the EVCS ecosystem, allowing for effective identification and mitigation of such attacks. The study highlights the need for decentralized detection methods to enhance grid resiliency and fault to

Author

digd0gx86

Language

EN