Exploiting the Potential Anomaly Detection in Automobile Safety Data with Multi-type Neural Network-Reference-Cited by-同舟云学术

Exploiting the Potential Anomaly Detection in Automobile Safety Data with Multi-type Neural Network

Published:2024 Issue: Volume: Page:489-501
ISSN:1867-8211
Container-title:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
language:en
Short-container-title:

Author:

Guan Quanlong^ORCID,Zhang Tian^ORCID,Huang Xiujie,Zhong Yuansheng,Du Cuifeng,Liu Changjiang,Li Zhefu,Zhang Guanghui,Wu Xiaofeng,Duan Zhifei

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-63989-0_26

Reference22 articles.

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3. Zhang, T., et al.: VSRQ: quantitative assessment method for safety risk of vehicle intelligent connected system (2023). arXiv: 2305.01898 [cs.AI]

4. Tomlinson, A., Bryans, J., Shaikh, S.A.: Towards viable intrusion detection methods for the automotive controller area network. In: 2nd ACM Computer Science in Cars Symposium, pp. 1–9 (2018)

5. Cheng, X., et al.: Influence-aware successive point-of-interest recommendation. In: World Wide Web, vol. 26, no. 2, pp. 615–629 (2023)

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