A Review of Intrusion Detection Methods for In-Vehicle Networks at the Semiconductor Level

Authors

  • Sujan Hiregundagal Gopal Rao

Keywords:

In-Vehicle Networks, Intrusion Detection Systems, Automotive Cybersecurity, CAN Bus, Semiconductor Security, Hardware-Based IDS

Abstract

The rapid digitalization of modern vehicles has significantly increased their exposure to cyber threats, particularly within in-vehicle networks (IVNs) such as the Controller Area Network (CAN). While numerous intrusion detection systems (IDSs) have been proposed at the software level, recent research highlights the growing importance of semiconductor-level intrusion detection to meet stringent real-time, safety, and reliability requirements. This paper presents a comprehensive review of intrusion detection methods for IVNs with a specific focus on hardware-assisted and semiconductor-integrated approaches. A structured literature review of recent studies is provided, followed by a comparative analysis of detection techniques, architectural implementations, and performance characteristics. The paper also discusses key challenges and future research directions toward secure, low-latency automotive semiconductor platforms.

DOI: https://doi.org/10.17762/ijisae.v11i10s.8000

Downloads

Download data is not yet available.

References

Kang, M.-J., & Kang, J.-W. (2016). Intrusion detection system using deep neural network for in-vehicle network security. PLOS ONE, 11(6), e0155781.

Taylor, A., Leblanc, S., & Japkowicz, N. (2020). Anomaly detection in automobile control network data with long short-term memory networks. IEEE Transactions on Intelligent Transportation Systems, 21(1), 128–137.

Wasicek, A., Weimerskirch, A., & Herber, C. (2021). Automotive cybersecurity: Hardware-assisted intrusion detection. SAE International Journal of Transportation Cybersecurity, 4(1), 85–95.

Muter, M., & Asaj, N. (2011). Entropy-based anomaly detection for in-vehicle networks. IEEE Intelligent Vehicles Symposium, 1110–1115.

Hoppe, T., Kiltz, S., & Dittmann, J. (2011). Security threats to automotive CAN networks. Automotive Cybersecurity, 235–248.

Seo, E., Song, H. M., & Kim, H. K. (2018). GIDS: GAN-based intrusion detection system for in-vehicle network. IEEE Access, 6, 41017–41026.

Studnia, I., Nicomette, V., Alata, E., & Deswarte, Y. (2015). Survey on security threats and protection mechanisms in embedded automotive networks. IEEE Transactions on Dependable and Secure Computing, 14(3), 316–329.

Miller, C., & Valasek, C. (2015). Remote exploitation of an unaltered passenger vehicle. Black Hat USA.

Bosch. (2021). Automotive Ethernet security challenges. Bosch Technical Report.

Wolf, M., Weimerskirch, A., & Paar, C. (2006). Security in automotive bus systems. Workshop on Embedded Security.

Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. IEEE Symposium on Security and Privacy.

Marchetti, M., & Stabili, D. (2018). Read: Reverse engineering of automotive data frames. IEEE Transactions on Information Forensics and Security, 14(4), 1083–1097.

Luo, H., et al. (2022). FPGA-based intrusion detection for CAN bus systems. IEEE Access, 10, 12456–12467.

Groza, B., Murvay, P.-S., & Van Herrewege, A. (2020). Security solutions for automotive CAN: A survey. IEEE Communications Surveys & Tutorials, 22(4), 2813–2839.

Wu, W., Chen, S., & Lin, C. (2023). Lightweight intrusion detection for automotive networks using hardware acceleration. Sensors, 23(7), 3610.

Downloads

Published

16.08.2023

How to Cite

Sujan Hiregundagal Gopal Rao. (2023). A Review of Intrusion Detection Methods for In-Vehicle Networks at the Semiconductor Level. International Journal of Intelligent Systems and Applications in Engineering, 11(10s), 1032–1036. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/8000

Issue

Vol. 11 No. 10s (2023)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.