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Ontology-Driven Modeling and Semantic Integration of Attack, Protection, and Risk Domains in Electric Vehicle Charging Systems

Lookup NU author(s): Talea HuraysiORCiD, Dr Trinadh PamulapatiORCiD, Kwabena Adu-Duodu, Professor Raj Ranjan, Dr Bo WeiORCiD, Dr Tejal Shah

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© 2026 by the authors. Electric Vehicle Charging Systems (EVCSs) have become a critical component of the global transition toward sustainable and intelligent transportation. However, their tight integration with heterogeneous cyber–physical, vehicular, and cloud-based infrastructures exposes them to an expanding attack surface, including data poisoning, malware injection, denial-of-service, and man-in-the-middle (MITM) attacks. Existing security solutions largely rely on isolated detection mechanisms and lack a unified semantic representation of EVCS assets, attack propagation paths, and mitigation dependencies, limiting their effectiveness in complex and evolving threat scenarios. To address these challenges, this paper proposes EVCS-SecOnt, an ontology-driven cybersecurity framework for modeling, reasoning, and mitigating security threats in EVCS infrastructures. The proposed ontology formalizes relationships across four core modules, namely Attack Surface, Attack Classification, Protection Mechanisms, and Risk and Mitigation, enabling holistic threat representation and TARA-based risk assessment. EVCS-SecOnt incorporates standard semantic namespaces (em:, seas:, uiote:, sch:, and time:) to ensure interoperability and is instantiated using the CICEVSE2024 dataset to support observation-level security reasoning. A unified SPARQL-based analytical workflow is employed to perform global ontology validation, attack–risk–severity correlation, mitigation prioritization, and observation-level inference using statistical feature vectors. Experimental results demonstrate that the ontology captures multiple attack classes, risk levels, severity categories, and mitigation strategies, enabling automated identification of critical attack scenarios and context-aware defense recommendations. The validation demonstrates logical consistency, semantic traceability, and query-based coverage of the ontology across attack classes, risk levels, severity categories, and mitigation strategies. EVCS-SecOnt enhances the interpretability, reusability, and explainability of EVCS cybersecurity management by bridging operational data with semantic intelligence. The proposed framework supports adaptive protection, risk-aware decision-making, and ontology-driven security analytics, providing a semantic foundation for next-generation e-mobility and smart charging infrastructures.

Publication metadata

Author(s): Huraysi T, Alsadi O, Pamulapati T, Adu-Duodu K, Ranjan R, Wei B, Shah T

Publication type: Article

Publication status: Published

Journal: Electronics

Year: 2026

Volume: 15

Issue: 16

Online publication date: 18/08/2026

Acceptance date: 15/08/2026

Date deposited: 07/09/2026

ISSN (electronic): 2079-9292

Publisher: MDPI

URL: https://doi.org/10.3390/electronics15163695

DOI: 10.3390/electronics15163695

Data Access Statement: The data used in this study are publicly available from the Canadian Institute for Cybersecurity (CIC), University of New Brunswick, as the CIC EV Charger Attack Dataset 2024 (CICEVSE2024). The dataset can be accessed at: https://www.unb.ca/cic/datasets/evse-dataset-2024.html, accessed on 20 August 2025. The ontology developed in this study was used exclusively for the analysis presented in this work and is available from the corresponding author upon reasonable request.

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