A Sensor Attack Detection Method in Intelligent Vehicle with Multiple Sensors

Abstract

With the rapid development of intelligent vehicles, more and more researchers are paying attention to their security issues. This paper considers a vehicle system with multiple sensors measuring the same physical variable. Some of these sensors may be maliciously attacked, resulting in the system being unable to work properly. This paper mainly addresses the detection and identification of malicious attacks on sensors in the presence of transient faults. Although there are some solutions at present, the existing methods can hardly capture attacks when a professional attacker manipulates the sensor output very slightly or infrequently. To address this problem, we design a resilient sensor attack detection algorithm. This algorithm adds an additional virtual sensor to the system and builds a fault model for each sensor, and using the pairwise inconsistencies between the sensors to detect the attack. To improve the detection rate in the presence of transient faults, we consider the system dynamics model and incorporates historical measurements into the detection algorithm. In addition, we propose a method to select transient fault model parameters to obtain a more accurate sensor fault model in a dynamic environment. Finally, this paper obtains real experimental data from the EV3 platform to verify the algorithm. Experimental results show that the proposed method is more robust and can detect and identify more attacks. Especially for stealth attacks which are extremely difficult to detect, the detection rate and recognition rate are increased by about 90%.