Multi-view graph contrastive representative learning for intrusion detection in EV charging station

Yi Li, Guo Chen and Zhaoyang Dong

Applied Energy, 2025, vol. 385, issue C, No S0306261925001692

Abstract: With the rapid proliferation of electric vehicles (EVs), the need to enhance EV charging infrastructure with integrated communication and software functionalities has become crucial. However, this integration also introduces new cybersecurity vulnerabilities, as sensitive data and operational control are increasingly exposed to potential attacks. Traditional intrusion detection systems often struggle with overfitting, low recall, and the scarcity of high-quality labeled data or fail to consider the correlation among different features, challenging the effectiveness of supervised learning approaches. To address these limitations, this paper proposes a novel Multi-View Graph Contrastive Representation Learning (MVGCRL) framework that leverages logs from Hardware Performance Counters (HPCs) collected from Electric Vehicle Supply Equipment (EVSE) and represents them as graph structure data. By constructing graph views for both hardware components and temporal windows, the framework utilizes a Graph Neural Network (GNN) model to capture correlations among various input features in a multi-view manner. This work designed a supervised intrusion detection system (IDS) for multi-class classification. Specifically, our method introduces hybrid graph augmentations through node feature masking and edge weight perturbation, and then employs a novel mask-attention Graph Transformer to capture complex feature correlations. Additionally, MVGCRL is extended to a self-supervised learning version by minimizing the distance between node embeddings and input features, followed by fine-tuning for improved classification. Experiments on real-world datasets demonstrate that our approach outperforms both traditional supervised methods and state-of-the-art self-supervised learning models, offering an effective solution for enhancing cybersecurity in EV charging infrastructures.

Keywords: Electric vehicle supply equipment; Cybersecurity; Intrusion detection; Graph constructive representative learning; Multi-class classification (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261925001692
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:385:y:2025:i:c:s0306261925001692

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2025.125439

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().