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Intrusion detection system based on the analysis of time intervals of CAN messages for in-vehicle network

Song, Hyun Min, Kim, Ha Rang, Kim, Huy Kang·2016·2016 International Conference on Information Networking (ICOIN)

Abstract

Controller Area Network (CAN) bus in the vehicles is a de facto standard for serial communication to provide an efficient, reliable and economical link between Electronic Control Units (ECU). However, CAN bus does not have enough security features to protect itself from inside or outside attacks. Intrusion Detection System (IDS) is one of the best ways to enhance the vehicle security level. Unlike the traditional IDS for network security, IDS for vehicle requires light-weight detection algorithm because of the limitations of the computing power of electronic devices reside in cars. In this paper, we propose a light-weight intrusion detection algorithm for in-vehicle network based on the analysis of time intervals of CAN messages. We captured CAN messages from the cars made by a famous manufacturer and performed three kinds of message injection attacks. As a result, we find the time interval is a meaningful feature to detect attacks in the CAN traffic. Also, our intrusion detection system detects all of message injection attacks without making false positive errors.

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Metadata

Authors
Song, H. M., Kim, H. R., & Kim, H. K.
Venue
2016 International Conference on Information Networking (ICOIN)
Publisher
IEEE
Published
2016-01
Pages
63-68
ISSN
1976-7684 (ISSN-L)
OpenAlex
W2296701710
Cited by
447
References
0
FWCI
32.94
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Intrusion detection system based on the analysis of time intervals of CAN messages for in-vehicle network. https://app.cochat.ai/papers/10.1109/icoin.2016.7427089
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Song, H. M., Kim, H. R., & Kim, H. K. (2016). Intrusion detection system based on the analysis of time intervals of CAN messages for in-vehicle network. 2016 International Conference on Information Networking (ICOIN). https://doi.org/10.1109/icoin.2016.7427089

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.