Chapter
Jul 2, 2019
Security Risk Assessment for Connected Vehicles Based on Back Propagation Neural Network
Authors: Yinghui Wang [email protected], Yunpeng Wang, Hongmao Qin [email protected], and Haojie JiAuthor Affiliations
Publication: CICTP 2019
Abstract
The wide application of information communication technology makes connected vehicles (CV) more vulnerable to be attacked. The communication systems and key nodes of CVs, e.g., electronic control units, controller area network (CAN), in-vehicle infotainment system, will face various threats such as eavesdropping, tampering, and counterfeiting. Cyber security of vehicles should be paid more attention because it involves drivers’ safety and public traffic security. It is impossible to deal with all kinds of security threats for the effect of performance, compatibility, cost, and efficiency of vehicles. Therefore, it is necessary to assess the security risk of CVs. This paper proposes a security risk assessment method based on the conventional security risk analysis model and utilized back propagation (BP) neural network. The simulation results show that the risk level of CVs can be evaluated quantitatively by trained neural networks, and the method is convenience and applicability for security risk assessment of vehicles.
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© 2019 American Society of Civil Engineers.
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Published online: Jul 2, 2019
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Affiliations
Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, School of Transportation Science and Engineering, Beihang Univ., Xue Yuan Rd. No. 37, HaiDian District, Beijing 100191, China. E-mail: [email protected]
Yunpeng Wang
Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, School of Transportation Science and Engineering, Beihang Univ., Xue Yuan Rd. No. 37, HaiDian District, Beijing 100191, China.
Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, School of Transportation Science and Engineering, Beihang Univ., Xue Yuan Rd. No. 37, HaiDian District, Beijing 100191, China. E-mail: [email protected]
Haojie Ji
Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, School of Transportation Science and Engineering, Beihang Univ., Xue Yuan Rd. No. 37, HaiDian District, Beijing 100191, China.
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