Intrusion Detection System for In-Vehicle CAN-FD Bus ID Based on GAN Model

Abstract

The growing abundance of electronic control units and peripheral devices loaded and connected to smart connected cars has resulted in a constant stream of cyber-attacks at various levels and dimensions. The CAN-FD bus plays a crucial role in smart connected cars. Currently, the majority of research efforts remain centered around the traditional CAN bus, with fewer studies addressing intrusion detection for the CAN-FD bus in smart connected vehicles. CAN-FD boasts a notable improvement in transmission speed, capable of reaching up to 8 Mbps compared to the 1 Mbps of the standard CAN bus. Utilizing intrusion detection systems designed for the CAN bus in high-speed CAN-FD applications could potentially hinder normal transmission and detection efficiency. Hence, we focus on the attack and intrusion detection of CAN-FD bus ID nodes to prevent unauthorized access and potential malicious attacks. We propose an ID intrusion detection system based on an improved Generative Adversarial Network (GAN) model, which consists of two parts: a data pre-processing module and a detection module. To apply the GAN model to the vehicle bus, we perform pre-processing of the bus data. We introduce the concept of dual discriminator to improve the detection rate and enable the handling of unknown attacks. With the output of dual discriminator, we can determine whether there are any anomalies in the detection data. First, we use a data pre-processing module to convert the ID segments of the automobile CAN-FD into binary image encoding to form ID images. Subsequently, these ID images are fed into an ID image feature extractor in the detection module to extract various auxiliary features. The discriminator receives these auxiliary features and calculates the probability of whether the received image is a normal ID image or not to determine the authenticity of the ID image. The experimental results show that the proposed intrusion detection system is able to detect a message within 0.15 ms, which fully meets the real-time detection requirements while the vehicle is in motion. The average detection rate of the proposed system for different types of attacks is 99.93%, which is an average of 1.2% improvement of the detection rate over the GIDS algorithm. The proposed system not only ensures the normal communication of CAN-FD bus but also realizes real-time and accurate intrusion detection.

Keywords

• Binary image coding
• CAN-FD
• dual discriminator
• generative adversarial network (GAN)
• ID image feature extractor
• network security