Open Access

ARTICLE

Data-Driven Digital Evidence Analysis for the Forensic Investigation of the Electric Vehicle Charging Infrastructure

Dong-Hyuk Shin¹, Jae-Jun Ha¹, Ieck-Chae Euom^2,*

1 System Security Research Center, Chonnam National University, Gwangju, 61186, Republic of Korea
2 Graduate School of DataScience, Chonnam National University, Gwangju, 61186, Republic of Korea

* Corresponding Author: Ieck-Chae Euom. Email:

(This article belongs to the Special Issue: Cutting-Edge Security and Privacy Solutions for Next-Generation Intelligent Mobile Internet Technologies and Applications)

Computer Modeling in Engineering & Sciences 2025, 143(3), 3795-3838. https://doi.org/10.32604/cmes.2025.066727

Received 16 April 2025; Accepted 13 June 2025; Issue published 30 June 2025

Abstract

The accelerated global adoption of electric vehicles (EVs) is driving significant expansion and increasing complexity within the EV charging infrastructure, consequently presenting novel and pressing cybersecurity challenges. While considerable effort has focused on preventative cybersecurity measures, a critical deficiency persists in structured methodologies for digital forensic analysis following security incidents, a gap exacerbated by system heterogeneity, distributed digital evidence, and inconsistent logging practices which hinder effective incident reconstruction and attribution. This paper addresses this critical need by proposing a novel, data-driven forensic framework tailored to the EV charging infrastructure, focusing on the systematic identification, classification, and correlation of diverse digital evidence across its physical, network, and application layers. Our methodology integrates open-source intelligence (OSINT) with advanced system modeling based on a three-layer cyber-physical system architecture to comprehensively map potential evidentiary sources. Key contributions include a comprehensive taxonomy of cybersecurity threats pertinent to EV charging ecosystems, detailed mappings between these threats and the resultant digital evidence to guide targeted investigations, the formulation of adaptable forensic investigation workflows for various incident scenarios, and a critical analysis of significant gaps in digital evidence availability within current EV charging systems, highlighting limitations in forensic readiness. The practical application and utility of this method are demonstrated through illustrative case studies involving both empirically-derived and virtual incident scenarios. The proposed data-driven approach is designed to significantly enhance digital forensic capabilities, support more effective incident response, strengthen compliance with emerging cybersecurity regulations, and ultimately contribute to bolstering the overall security, resilience, and trustworthiness of this increasingly vital critical infrastructure.

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Keywords

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