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Open Access

ARTICLE

Anomaly Detection of Controllable Electric Vehicles through Node Equation against Aggregation Attack

Jing Guo*, Ziying Wang, Yajuan Guo, Haitao Jiang
State Grid Jiangsu Electric Power Co., Ltd. Research Institute, State Grid Corporation of China, Nanjing, 211100, China
* Corresponding Author: Jing Guo. Email: email
(This article belongs to the Special Issue: Best Practices for Smart Grid SCADA Security Systems Using Artificial Intelligence (AI) Models)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.057045

Received 06 August 2024; Accepted 11 October 2024; Published online 07 November 2024

Abstract

The rapid proliferation of electric vehicle (EV) charging infrastructure introduces critical cybersecurity vulnerabilities to power grids system. This study presents an innovative anomaly detection framework for EV charging stations, addressing the unique challenges posed by third-party aggregation platforms. Our approach integrates node equations-based on the parameter identification with a novel deep learning model, xDeepCIN, to detect abnormal data reporting indicative of aggregation attacks. We employ a graph-theoretic approach to model EV charging networks and utilize Markov Chain Monte Carlo techniques for accurate parameter estimation. The xDeepCIN model, incorporating a Compressed Interaction Network, has the ability to capture complex feature interactions in sparse, high-dimensional charging data. Experimental results on both proprietary and public datasets demonstrate significant improvements in anomaly detection performance, with F1-scores increasing by up to 32.3% for specific anomaly types compared to traditional methods, such as wide & deep and DeepFM (Factorization-Machine). Our framework exhibits robust scalability, effectively handling networks ranging from 8 to 85 charging points. Furthermore, we achieve real-time monitoring capabilities, with parameter identification completing within seconds for networks up to 1000 nodes. This research contributes to enhancing the security and reliability of renewable energy systems against evolving cyber threats, offering a comprehensive solution for safeguarding the rapidly expanding EV charging infrastructure.

Keywords

Anomaly detection; electric vehicle; aggregation attack; deep cross-network

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