TY - GEN
T1 - Security risk assessment for connected vehicles based on back propagation neural network
AU - Wang, Yinghui
AU - Wang, Yunpeng
AU - Qin, Hongmao
AU - Ji, Haojie
N1 - Publisher Copyright:
© ASCE.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Back propagation neural network
KW - Connected vehicles
KW - Fuzzy theory
KW - Security risk assessment
UR - https://www.scopus.com/pages/publications/85070288135
U2 - 10.1061/9780784482292.493
DO - 10.1061/9780784482292.493
M3 - 会议稿件
AN - SCOPUS:85070288135
T3 - CICTP 2019: Transportation in China - Connecting the World - Proceedings of the 19th COTA International Conference of Transportation Professionals
SP - 5733
EP - 5745
BT - CICTP 2019
A2 - Zhang, Lei
A2 - Ma, Jianming
A2 - Liu, Pan
A2 - Zhang, Guangjun
PB - American Society of Civil Engineers (ASCE)
T2 - 19th COTA International Conference of Transportation Professionals: Transportation in China - Connecting the World, CICTP 2019
Y2 - 6 July 2019 through 8 July 2019
ER -