Vehicle Cybersecurity: Methods, Datasets, and Deployment Gaps
DOI:
https://doi.org/10.18196/jrc.v6i5.27477Keywords:
Vehicle Cybersecurity, Anti-Theft Systems, Intrusion Detection, V2X Security, Blockchain, Driver Monitoring, Federated LearningAbstract
Modern Connected and automated vehicles expand the attack surface across in-vehicle networks and V2X links, making robust, evidence-based protection essential. This article presents a systematic review of vehicle cybersecurity, consolidating methods, datasets, and deployment barriers to inform research and practice. The research contribution is a focused synthesis (2020-2025) that (i) maps dominant technical themes (intrusion detection, secure in-vehicle networking, V2X protection, secure OTA, privacy), (ii) catalogs datasets and evaluation practices, and (iii) identifies reproducibility and real-world validation gaps that constrain deployment. Following PRISMA, we searched Scopus, IEEE Xplore, Web of Science, and MDPI within 2020-2025 using predefined Boolean strings. Inclusion targeted vehicle-focused cybersecurity methods or systems with empirical evaluation (simulation, testbed, or field); exclusions removed non-vehicular, purely conceptual, or duplicate records. Data extraction covered threat models, algorithm families, datasets, metrics, and deployment context; study quality and risk of bias were appraised using a standardized rubric. Results show progress in ML-based intrusion detection, cryptography and key management, authentication/access control, secure OTA updates, and V2X security; however, heterogeneous datasets and metrics limit cross-study comparability, and evaluations remain predominantly offline or lab-based. Bridging lab-to-road requires standardized datasets and metrics, multi-site and on-road evaluations, and co-assurance of security with safety and privacy requirements; we outline an actionable roadmap to accelerate deployment while mitigating methodological and publication biases.
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