Abstract
The implementation of the car driving identification system will help to improve the safety and efficiency of navigation. The safety analysis technology can filter the attack events of the automatic identification system of car driving and reduce the error rate of safety analysis. To this end, a deep learning-based safety analysis method for car driving identification system is proposed. Based on the deep learning theory, a safety behavior analysis model of the car driving identification system is constructed. Through data collection, security analysis and response processing, the identification of abnormal communication security data strength is completed. A Cartesian coordinate system is established, and a heterogeneous data processing model is constructed. Based on the deep learning analysis process of security data, based on deep learning, by accessing system operation data, stream processing and data mining of security data, complete system security data defense control, and realize system security analysis. The experimental results show that the method in this paper has strong security analysis ability, and its matching range is large, which can match all security behaviors and reduce the error rate of security analysis.
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Acknowledgement
1. Science and Technology Youth Program of Chongqing Education Commission in 2020:Design of A pillar blind zone vision system for passenger cars (Project No.: KJQN202004602).
2021 Chongqing Vocational Institute of Tourism Mass Innovation Space Project:Universal Vision Given by Science and Technology -- Development of Visual System for Blind Zone of Passenger Car A Pillar (Project No.: 2021DC01).
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© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wei, X., Zhang, R. (2024). Security Analysis of Car Driving Identification System Based on Deep Learning. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 532. Springer, Cham. https://doi.org/10.1007/978-3-031-50571-3_34
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DOI: https://doi.org/10.1007/978-3-031-50571-3_34
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