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OTIDS: A Novel Intrusion Detection System for In-vehicle Network by Using Remote Frame
Abstract
Controller Area Network (CAN) is a bus communication protocol which defines a standard for reliable and efficient transmission between in-vehicle nodes in real-time. Since CAN message is broadcast from a transmitter to the other nodes on a bus, it does not contain information about the source and destination address for validation. Therefore, an attacker can easily inject any message to lead system malfunctions. In this paper, we propose an intrusion detection method based on the analysis of the offset ratio and time interval between request and response messages in CAN. If a remote frame having a particular identifier is transmitted, a receiver node should respond to the remote frame immediately. In attack-free state, each node has a fixed response offset ratio and time interval while these values vary in attack state. Using this property, we can measure the response performance of the existing nodes based on the offset ratio and time interval between request and response messages. As a result, our methodology can detect intrusions by monitoring offset ratio and time interval, and it allows quick intrusion detection with high accuracy.
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Cited by
GIDS: GAN based Intrusion Detection System for In-Vehicle Network
Seo, E., Song, H. M., & Kim, H. K. (2018) · 2018 16th Annual Conference on Privacy, Security and Trust (PST) · doi:10.1109/pst.2018.8514157 · Cited by 533
Self-citationA Survey of Intrusion Detection for In-Vehicle Networks
Wu, W., Li, R., Xie, G., An, J., Bai, Y., Zhou, J., & Li, K. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2908074 · Cited by 394
Javed, A. R., Rehman, S. U., Khan, M. U., Alazab, M., & G, T. R. (2021) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2021.3059881 · Cited by 331
Ashraf, J., Bakhshi, A. D., Moustafa, N., Khurshid, H., Javed, A., & Beheshti, A. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3017882 · Cited by 295
Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks
Mehedi, S. T., Anwar, A., Rahman, Z., & Ahmed, K. (2021) · Sensors · doi:10.3390/s21144736 · Cited by 150
Metadata
- Authors
- Lee, H., Jeong, S. H., & Kim, H. K.
- Venue
- 2017 15th Annual Conference on Privacy, Security and Trust (PST)
- Publisher
- IEEE
- Published
- 2017-08
- Pages
- 57-5709
- OpenAlex
- W2897929592
- Cited by
- 386
- References
- 0
- FWCI
- 16.66
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Lee, H., Jeong, S. H., & Kim, H. K. (2017). OTIDS: A Novel Intrusion Detection System for In-vehicle Network by Using Remote Frame. 2017 15th Annual Conference on Privacy, Security and Trust (PST). https://doi.org/10.1109/pst.2017.00017
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