Lightweight CNN-Based Intrusion Detection for Automotive CAN Bus in Light Commercial Vehicles

Emre Tüfekcioğlu; Cemal Hanilçi; Hakan Gürkan

doi:10.38088/jise.1717959

Lightweight CNN-Based Intrusion Detection for Automotive CAN Bus in Light Commercial Vehicles

Abstract

With the rapid advancement of digitalization and automation, modern vehicles, especially in the light commercial segment, have evolved into complex, interconnected platforms resembling mobile computing systems. This transformation has increased the dependency on in-vehicle communication networks and, as a result, exposed them to a wider range of cybersecurity threats. A fundamental aspect of the proposed method is the use of a lightweight CNN model specific for deployment in embedded automotive environments with limited computational resources and optimized for efficiency. Operating on low-power hardware platforms such as edge ECUs, the tiny device developed in this study works effectively unlike conventional deep learning architectures seeking high processing power and memory. Despite its minimal computational footprint, the model is capable of accurately distinguishing between legitimate and spoofed communication traffic, as well as detecting a variety of attack forms that target different CAN protocol components. The performance metrics of the model further highlight its effectiveness, achieving a ROC AUC Score of 0.9887, an Accuracy of 0.9887, a Precision of 0.9825, a Recall of 0.9952, and an F1-Score of 0.9888. Particularly for real-time on-vehicle intrusion detection systems, this harmony between performance and efficiency makes the strategy especially important. Just as importantly is the introduction of a specifically produced hybrid dataset, which is fundamental for system evaluation and training. The dataset aggregates synthetic generated attack scenarios with real-world spoofing, injection, and denial-of- service (DoS) conditions using actual CAN traffic acquired from a J1939-compliant light commercial vehicle. Standard 11-bit identities combined with industrial communication protocols help the dataset to reflect real-world vehicle dynamics across several ECUs under various scenarios. The model can learn fine-grained patterns often missed by conventional rule-based or manually engineered approaches by means of the image-like transformation of CAN messages—preserving bit-level and temporal information. In intelligent transportation systems, the lightweight CNN architecture and the strong dataset combine to create a scalable and deployable IDS framework that can improve in-vehicle cybersecurity.

Keywords

Thanks

We would want to sincerely thank you to Karsan for their essential contribution in allowing the data collecting process. Particularly, we would want to thank Mr. M. Alper BALIM, E&E Vehicle Software Engineering Administrator, and Mr. Nurettin ÖZEKMEKCİ, Manager of E&E Design Engineering, whose priceless direction, insights, and ongoing support have greatly helped this research to be successful. We also want to thank the whole Karsan R&D team for their kind cooperation, test vehicle preparation, and guarantee of access to the required tools and systems for experimental use. Their dedication and transparency made it feasible to create an industrial-grade, realistic dataset and to do all stages of this work under reasonable, dependable surroundings.

References

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Details

Primary Language

English

Subjects

Deep Learning

Journal Section

Research Article

Authors

Emre Tüfekcioğlu ^*
0000-0002-1897-2460
Türkiye

Cemal Hanilçi
0000-0002-9174-0367
Türkiye

Hakan Gürkan
0000-0002-7008-4778
Türkiye

Early Pub Date

September 30, 2025

Publication Date

December 15, 2025

Submission Date

June 11, 2025

Acceptance Date

July 7, 2025

Published in Issue

Year 2025 Volume: 9 Number: 2

DOI

https://doi.org/10.38088/jise.1717959

IZ

https://izlik.org/JA99SP34PA

Cite

RIS / Bibtex

APA

Tüfekcioğlu, E., Hanilçi, C., & Gürkan, H. (2025). Lightweight CNN-Based Intrusion Detection for Automotive CAN Bus in Light Commercial Vehicles. Journal of Innovative Science and Engineering, 9(2), 259-267. https://doi.org/10.38088/jise.1717959

AMA

1.Tüfekcioğlu E, Hanilçi C, Gürkan H. Lightweight CNN-Based Intrusion Detection for Automotive CAN Bus in Light Commercial Vehicles. JISE. 2025;9(2):259-267. doi:10.38088/jise.1717959

Chicago

Tüfekcioğlu, Emre, Cemal Hanilçi, and Hakan Gürkan. 2025. “Lightweight CNN-Based Intrusion Detection for Automotive CAN Bus in Light Commercial Vehicles”. Journal of Innovative Science and Engineering 9 (2): 259-67. https://doi.org/10.38088/jise.1717959.

EndNote

Tüfekcioğlu E, Hanilçi C, Gürkan H (December 1, 2025) Lightweight CNN-Based Intrusion Detection for Automotive CAN Bus in Light Commercial Vehicles. Journal of Innovative Science and Engineering 9 2 259–267.

IEEE

[1]E. Tüfekcioğlu, C. Hanilçi, and H. Gürkan, “Lightweight CNN-Based Intrusion Detection for Automotive CAN Bus in Light Commercial Vehicles”, JISE, vol. 9, no. 2, pp. 259–267, Dec. 2025, doi: 10.38088/jise.1717959.

ISNAD

Tüfekcioğlu, Emre - Hanilçi, Cemal - Gürkan, Hakan. “Lightweight CNN-Based Intrusion Detection for Automotive CAN Bus in Light Commercial Vehicles”. Journal of Innovative Science and Engineering 9/2 (December 1, 2025): 259-267. https://doi.org/10.38088/jise.1717959.

JAMA

1.Tüfekcioğlu E, Hanilçi C, Gürkan H. Lightweight CNN-Based Intrusion Detection for Automotive CAN Bus in Light Commercial Vehicles. JISE. 2025;9:259–267.

MLA

Tüfekcioğlu, Emre, et al. “Lightweight CNN-Based Intrusion Detection for Automotive CAN Bus in Light Commercial Vehicles”. Journal of Innovative Science and Engineering, vol. 9, no. 2, Dec. 2025, pp. 259-67, doi:10.38088/jise.1717959.

Vancouver

1.Emre Tüfekcioğlu, Cemal Hanilçi, Hakan Gürkan. Lightweight CNN-Based Intrusion Detection for Automotive CAN Bus in Light Commercial Vehicles. JISE. 2025 Dec. 1;9(2):259-67. doi:10.38088/jise.1717959