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.

中文翻译：

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基于图形的控制器局域网入侵检测系统

控制器局域网（CAN）是汽车行业应用最广泛的车内通信网络。由于其设计简单，它缺乏经过安全验证的通信协议所需的大部分要求。然而，安全可靠的环境对于自动驾驶和联网车辆来说必不可少。因此，CAN 安全性被认为是汽车研究界的重要课题之一。在本文中，我们提出了一种四阶段入侵检测系统，该系统使用卡方方法，可以检测 CAN 中任何类型的强弱网络攻击。这项工作是有史以来第一个为 CAN 提出的基于图的防御系统。我们的实验结果表明，拒绝服务 (DoS) 攻击的错误分类率为 5.26%，模糊攻击的错误分类率为 10%，4。76% 重放攻击错误分类，欺骗攻击没有错误分类。此外，与现有的基于 ID 序列的方法相比，所提出的方法的准确性提高了 13.73%。