# Edge-Based Detection and Localization of Adversarial Oscillatory Load Attacks Orchestrated By Compromised EV Charging Stations

## TL;DR

This paper presents an edge-based deep learning approach for detecting and localizing malicious oscillatory load attacks from compromised EV charging stations, enhancing grid resilience and enabling rapid mitigation.

## Contribution

It introduces a decentralized detection framework using deep learning, specifically CNN-LSTM models, and a real-time test bed for effective attack identification and mitigation in EV charging ecosystems.

## Key findings

- Detection accuracy of 99.4% with CNN-LSTM model
- Effective attack localization and mitigation strategies
- Real-time test bed demonstrates rapid recovery within 1 second

## Abstract

In this paper, we investigate an edge-based approach for the detection and localization of coordinated oscillatory load attacks initiated by exploited EV charging stations against the power grid. We rely on the behavioral characteristics of the power grid in the presence of interconnected EVCS while combining cyber and physical layer features to implement deep learning algorithms for the effective detection of oscillatory load attacks at the EVCS. We evaluate the proposed detection approach by building a real-time test bed to synthesize benign and malicious data, which was generated by analyzing real-life EV charging data collected during recent years. The results demonstrate the effectiveness of the implemented approach with the Convolutional Long-Short Term Memory model producing optimal classification accuracy (99.4\%). Moreover, our analysis results shed light on the impact of such detection mechanisms towards building resiliency into different levels of the EV charging ecosystem while allowing power grid operators to localize attacks and take further mitigation measures. Specifically, we managed to decentralize the detection mechanism of oscillatory load attacks and create an effective alternative for operator-centric mechanisms to mitigate multi-operator and MitM oscillatory load attacks against the power grid. Finally, we leverage the created test bed to evaluate a distributed mitigation technique, which can be deployed on public/private charging stations to average out the impact of oscillatory load attacks while allowing the power system to recover smoothly within 1 second with minimal overhead.