论文标题
基于图形的控制器区域网络的入侵检测系统
Graph-Based Intrusion Detection System for Controller Area Networks
论文作者
论文摘要
控制器区域网络（CAN）是汽车行业中使用最广泛的车辆内通信网络。由于其在设计方面的简单性，它缺乏安全性通信协议所需的大多数要求。但是，对于自动驾驶汽车和连接的车辆，必须使用安全和安全的环境。因此，安全性被认为是汽车研究界的重要主题之一。在本文中，我们提出了一个使用卡方方法的四阶段入侵检测系统，可以检测罐头中的任何强大和弱的网络攻击。这项工作是为罐头提出的有史以来第一个基于图的防御系统。我们的实验结果表明，拒绝服务（DOS）攻击的5.26％错误分类，模糊攻击的10％错误分类，重播攻击的错误分类为4.76％，并且没有用于欺骗攻击的错误分类。此外，与现有的基于ID序列的方法相比，提出的方法的准确性高达13.73％。
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.
