A Study on Recurrent Neural Network Performance for In-Vehicle Intrusion Detection Agents 


Vol. 50,  No. 12, pp. 1842-1845, Dec.  2025
10.7840/kics.2025.50.12.1842


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  Abstract

The lack of security mechanism of Controller Area Network (CAN) bus protocol introduces structural vulnerabilities, requiring intrusion detection agents for message injection attacks. This study presents an intrusion detection system based on Recurrent Neural Networks (RNNs), capturing time-series patterns. We compare the performance of various RNNs and analyze how each architecture influences its detection capability.

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[IEEE Style]

S. Park, D. Choi, H. Park, "A Study on Recurrent Neural Network Performance for In-Vehicle Intrusion Detection Agents," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 12, pp. 1842-1845, 2025. DOI: 10.7840/kics.2025.50.12.1842.

[ACM Style]

Siyoun Park, Dayoung Choi, and Hyunggon Park. 2025. A Study on Recurrent Neural Network Performance for In-Vehicle Intrusion Detection Agents. The Journal of Korean Institute of Communications and Information Sciences, 50, 12, (2025), 1842-1845. DOI: 10.7840/kics.2025.50.12.1842.

[KICS Style]

Siyoun Park, Dayoung Choi, Hyunggon Park, "A Study on Recurrent Neural Network Performance for In-Vehicle Intrusion Detection Agents," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 12, pp. 1842-1845, 12. 2025. (https://doi.org/10.7840/kics.2025.50.12.1842)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880