TY - GEN
T1 - Cyber-Attack Detection Framework for Connected Vehicles in V2X Networks Based on An Iterative UFIR Filter
AU - Jiang, Kai
AU - Ju, Zhiyang
AU - Huang, Lingying
AU - Su, Rong
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Connected vehicles have great advantages in driving safety and energy efficiency under the support of vehicle-to-everything (V2X) networks, while they are also vulnerable to malicious cyber-attacks. To enhance the cyber security of connected vehicles, a cyber-attack detection framework is proposed based on multi-source information fusion specifically for the vehicle localization system. In this framework, an iterative unbiased finite impulse response (UFIR) filter is utilized to estimate the vehicle position with low computational load, based on the vehicle dynamics model and information from the inertial measurement system (IMU), GPS, and V2X networks. In addition, a discriminator module is developed to analyze the residuals between estimations and position information from different sources for cyber-attack detection. Finally, multiple simulation cases are implemented to validate the effectiveness of the proposed framework.
AB - Connected vehicles have great advantages in driving safety and energy efficiency under the support of vehicle-to-everything (V2X) networks, while they are also vulnerable to malicious cyber-attacks. To enhance the cyber security of connected vehicles, a cyber-attack detection framework is proposed based on multi-source information fusion specifically for the vehicle localization system. In this framework, an iterative unbiased finite impulse response (UFIR) filter is utilized to estimate the vehicle position with low computational load, based on the vehicle dynamics model and information from the inertial measurement system (IMU), GPS, and V2X networks. In addition, a discriminator module is developed to analyze the residuals between estimations and position information from different sources for cyber-attack detection. Finally, multiple simulation cases are implemented to validate the effectiveness of the proposed framework.
KW - Connected vehicles
KW - Cyber-attack detection
KW - Unbiased finite impulse response filter
KW - V2X networks
UR - http://www.scopus.com/inward/record.url?scp=85184812514&partnerID=8YFLogxK
U2 - 10.1109/CDC49753.2023.10383926
DO - 10.1109/CDC49753.2023.10383926
M3 - Conference contribution
AN - SCOPUS:85184812514
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 86
EP - 91
BT - 2023 62nd IEEE Conference on Decision and Control, CDC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 62nd IEEE Conference on Decision and Control, CDC 2023
Y2 - 13 December 2023 through 15 December 2023
ER -
