Intrusion Detection System Based on Deep Neural Network and Incremental Learning for In-Vehicle CAN Networks
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Publisher
Springer Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-0468-4_19
Reference19 articles.
1. Wu, F.: A survey of intrusion detection for in-vehicle networks. IEEE Trans. Intell. Transp. Syst. 21(3), 919–933 (2020)
2. Lokman, S.-F.: Intrusion detection system for automotive Controller Area Network (CAN) bus system: a review. EURASIP J. Wirel. Commun. Netw. 2019, 184 (2019)
3. Lee, H., Jeong, S.H., Kim, S.: OTIDS: a novel intrusion detection system for in-vehicle network by using remote frame. In: 2017 15th Annual Conference on Privacy, Security and Trust (PST), IEEE, Piscataway (2017)
4. Cho, K.-T., Shin, K.: Viden: Attacker identification on in-vehicle networks. In: 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1109–1123. ACM, New York (2017)
5. Subir Halder, F., Mauro Conti, S.: COIDS: a clock offset based intrusion detection system for controller area networks. In: 21st International Conference on Distributed Computing and Networking (ICDCN 2020), pp. 1–10. ACM, New York (2020)
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