In-Vehicle Network Attack Detection Across Vehicle Models
Author:
Affiliation:
1. Graduate School of Informatics, Kyoto University
2. Panasonic Holdings Corporation
Publisher
Japanese Society for Artificial Intelligence
Subject
Artificial Intelligence,Software
Link
https://www.jstage.jst.go.jp/article/tjsai/37/5/37_37-5_C-M42/_pdf
Reference24 articles.
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2. [Braga 10] R. Braga, E. Mota, and A. Passito: Lightweight DDoS flooding attack detection using NOX/OpenFlow, in Proceedings of the 35th IEEE Local Computer Network Conference (LCN), pp. 408–415 (2010)
3. [Breunig 00] M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander: LOF: identifying density-based local outliers, in Proceedings of the ACM International Conference on Management of Data (SIGMOD), pp. 93–104 (2000)
4. [Chandola 09] V. Chandola, A. Banerjee, and V. Kumar: Anomaly detection: A survey, ACM Computing Surveys (CSUR), Vol. 41, No. 3, pp. 1–58 (2009)
5. [Cho 16] K.-T. Cho and K. G. Shin : Fingerprinting electronic control units for vehicle intrusion detection, in Proceedings of the 25th USENIX Security Symposium (USENIX Security), pp. 911–927 (2016)
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