Graph-Based Intrusion Detection System for Controller Area Networks
This addresses security vulnerabilities in automotive CAN networks, which are critical for autonomous and connected vehicles, but the approach is incremental as it builds on graph-based methods for a specific domain.
The paper tackles the problem of securing controller area networks (CAN) in vehicles by proposing a four-stage intrusion detection system using the chi-squared method, achieving low misclassification rates (e.g., 5.26% for DoS attacks) and up to 13.73% better accuracy than existing methods.
The controller area network (CAN) is the most widely used intra-vehicular communication network in the automotive industry. Because of its simplicity in design, it lacks most of the requirements needed for a security-proven communication protocol. However, a safe and secured environment is imperative for autonomous as well as connected vehicles. Therefore CAN security is considered one of the important topics in the automotive research community. In this paper, we propose a four-stage intrusion detection system that uses the chi-squared method and can detect any kind of strong and weak cyber attacks in a CAN. This work is the first-ever graph-based defense system proposed for the CAN. Our experimental results show that we have a very low 5.26% misclassification for denial of service (DoS) attack, 10% misclassification for fuzzy attack, 4.76% misclassification for replay attack, and no misclassification for spoofing attack. In addition, the proposed methodology exhibits up to 13.73% better accuracy compared to existing ID sequence-based methods.