The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Anomaly detection of CAN bus messages through analysis of ID sequences
Open accessAbstract
This paper proposes a novel intrusion detection algorithm that aims to identify malicious CAN messages injected by attackers in the CAN bus of modern vehicles. The proposed algorithm identifies anomalies in the sequence of messages that flow in the CAN bus and is characterized by small memory and computational footprints, that make it applicable to current ECUs. Its detection performance are demonstrated through experiments carried out on real CAN traffic gathered from an unmodified licensed vehicle.
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Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
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
A 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
Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection
Wang, Z., Chen, Z., Ni, J., Liu, H., Chen, H., & Tang, J. (2021) · Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining · doi:10.1145/3447548.3467125 · Cited by 56
MADDC: Multi-Scale Anomaly Detection, Diagnosis and Correction for Discrete Event Logs
Wang, X., Yang, L., Li, D., Ma, L., He, Y., Xiao, J., Liu, J., & Yang, Y. (2022) · Proceedings of the 38th Annual Computer Security Applications Conference · doi:10.1145/3564625.3567972 · Cited by 16
Das, R., Zhou, L., Bi, S., Wang, T., & Hou, T. (2026) · ACM Transactions on Sensor Networks · doi:10.1145/3786592 · Cited by 7
Metadata
- Authors
- Marchetti, M., & Stabili, D.
- Venue
- 2017 IEEE Intelligent Vehicles Symposium (IV)
- Publisher
- IEEE
- Published
- 2017-06
- Pages
- 1577-1583
- ISSN
- 1931-0587 (ISSN-L)
- OpenAlex
- W2739928414
- Cited by
- 239
- References
- 1
- FWCI
- 14.90
- Type
- conference paper
- Language
- en
- Open access
- green
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- Marchetti, M., & Stabili, D. (2017). Anomaly detection of CAN bus messages through analysis of ID sequences. 2017 IEEE Intelligent Vehicles Symposium (IV). https://doi.org/10.1109/ivs.2017.7995934
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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.
