Research Article
Exploiting the Potential Anomaly Detection in Automobile Safety Data with Multi-type Neural Network
@INPROCEEDINGS{10.1007/978-3-031-63989-0_26, author={Quanlong Guan and Tian Zhang and Xiujie Huang and Yuansheng Zhong and Cuifeng Du and Changjiang Liu and Zhefu Li and Guanghui Zhang and Xiaofeng Wu and Zhifei Duan}, title={Exploiting the Potential Anomaly Detection in Automobile Safety Data with Multi-type Neural Network}, proceedings={Mobile and Ubiquitous Systems: Computing, Networking and Services. 20th EAI International Conference, MobiQuitous 2023, Melbourne, VIC, Australia, November 14--17, 2023, Proceedings, Part I}, proceedings_a={MOBIQUITOUS}, year={2024}, month={7}, keywords={CAN bus network Intrusion detection Network hackers Multi-classification}, doi={10.1007/978-3-031-63989-0_26} }- Quanlong Guan
Tian Zhang
Xiujie Huang
Yuansheng Zhong
Cuifeng Du
Changjiang Liu
Zhefu Li
Guanghui Zhang
Xiaofeng Wu
Zhifei Duan
Year: 2024
Exploiting the Potential Anomaly Detection in Automobile Safety Data with Multi-type Neural Network
MOBIQUITOUS
Springer
DOI: 10.1007/978-3-031-63989-0_26
Abstract
As an internal network widely used in automobiles, the automotive CAN bus network lacks effective security protection mechanisms and is vulnerable to network hackers, posing a serious threat to the safety of vehicles and drivers. The automotive intrusion detection system provides effective protection for the security of the automotive CAN network. To address the shortcomings of current intrusion detection algorithms, such as long application time and incomplete detection types, GIDPS and TIDPS models are proposed to perform supervised multi-classification experiments on vehicle intrusion data. Then, the above model is migrated to the ROAD dataset for verification, and the advantages of the new model in terms of time and accuracy compared with the old model are analysed based on the results. The proposed GIDPS and TIDPS models achieve better results than previous models in terms of synthesis. The new models provides a certain reference value for improving the level of automotive network security. They could be applied to domestic or cross-border automotive markets.
