EVECTOR: An Orchestrator for Analysing Attacks in Electric Vehicle Charging Systems

Jha, Devki Nandan, Szydlo, Tomasz, Valizadeh, Nima ORCID: https://orcid.org/0009-0009-9316-8278, Sham, Ringo, Edwards, Aleksandra, Kumar, Amrit, Kaur, Amanjot, Wei, Bo, Kumar, Vijay, Lim, Kai Li, Ranjan, Rajiv and Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646 2026. EVECTOR: An Orchestrator for Analysing Attacks in Electric Vehicle Charging Systems. IET Intelligent Transport Systems 20 (1) , e70324. 10.1049/itr2.70324

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Abstract

Electric vehicle (EV) charging infrastructure is critical for the widespread adoption of EVs, ensuring efficient and secure charging processes. Evaluating the security and performance of EV charging systems in real-world infrastructure poses significant challenges due to the diversity of information exchange between vehicles and charging stations/electric vehicle supply equipment (EVSE), including complex network protocols, scale of deployment and a variety of potential threats. Existing simulation frameworks are unable to handle complex security scenarios across these differing data exchange protocols. In this paper, we propose a novel EV orchestration framework: EVECTOR, which addresses the limitations of existing simulation systems by enabling both quantitative and qualitative analyses of EV charging scenarios. EVECTOR (i) combines open-source EV charging simulators, including EVerest and ISO 15118 to support the analysis of a holistic EV charging ecosystem, (ii) provides a simulator-agnostic attack abstraction that lets one attack scenario be expressed once and executed against heterogeneous backends at different layers, (iii) offers a common telemetry schema that normalises logs from event-driven simulators for cross-layer analysis. Moreover, EVECTOR has a modular extension mechanism that lets new simulators or attacks be added without affecting the core orchestrator. We validate the EVECTOR framework through two case studies: (a) a cyber-physical attack, broken wire and (b) a cyber-specific attack, frame fuzzification. The case studies highlight EVECTOR's effectiveness in providing deeper insights into the security and performance of EV charging systems.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Computational & Mathematical Sciences Schools > Computer Science & Informatics
Publisher:	Institution of Engineering and Technology (IET)
ISSN:	1751-956X
Date of First Compliant Deposit:	14 September 2026
Date of Acceptance:	20 August 2026
Last Modified:	14 Sep 2026 14:15
URI:	https://orca.cardiff.ac.uk/id/eprint/189563

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