A Systematic Review and Meta-analysis Survey of IDS/IPS Techniques for CAN and Vehicular Networks

Younis Abdo Mohammed Nasser Al Shojaa; Khaled Al Soufy

doi:10.59167/sbta1126

Authors

Younis Abdo Mohammed Nasser Al Shojaa Information Technology Department, Faculty of Engineering and Information Technology, Al-Qalam University, Ibb, Yemen. Author
Khaled Al Soufy Electrical Engineering Department, Faculty of Engineering, Ibb University, Ibb, Yemen. Author

DOI:

https://doi.org/10.59167/sbta1126

Keywords:

CAN Bus, Vehicular Networks, In-Vehicle Communication, Intrusion Detection System, Sensitivity Analysis, Machine Learning, Deep Learning, Automotive Cybersecurity

Abstract

As cyberattacks grow increasingly sophisticated and frequent, the need for advanced mechanisms to protect modern network systems has become more pressing, especially in sensitive environments such as automotive networks. This research presents a systematic review, complemented by a sensitivity-based quantitative trend analysis, of recent efforts to employ Machine Learning (ML) and Deep Learning (DL) techniques for intrusion detection and prevention within automotive networks, with a particular focus on the Controller Area Network (CAN) bus. The research employs a clear and reproducible methodology for identifying, screening, and evaluating relevant studies, guided by the PRISMA framework. It also presents a structured classification that groups current approaches according to model type, data sources, and evaluation strategies. Furthermore, the review provides a comparative analysis highlighting the key strengths, weaknesses, and experimental performance of various IDS/IPS techniques in automotive environments. In addition, the study includes a sensitivity-based quantitative summary of reported accuracy patterns in the literature, offering an exploratory and sensitivity-aware view of performance trends rather than a formal pooled estimate. The analysis encompassed 56 studies published between 2018 and 2025, covering diverse datasets, methodological designs, and multiple detection objectives. The findings reveal ongoing challenges, including real-time limitations, limited computational resources, and dataset variability, while also pointing to promising research avenues that could contribute to the development of more efficient AI-based intrusion detection and prevention systems for connected and autonomous vehicles.

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