Vehicle Cybersecurity: Methods, Datasets, and Deployment Gaps

Bonifacio R.F.A.; Milla B.Y.B.; Rojas J.J.H. · Journal of Robotics and Control (JRC) (2025) · DOI: 10.18196/jrc.v6i5.27477

Resumen

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 realworld 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 onroad 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.

Instituciones: Universidad Continental