Affiliation:
1. School of Automotive Studies, Tongji University, Shanghai 201804, China
2. Nanchang Automotive Institute of Intelligence and New Energy, Tongji University (NAIT), Nanchang 330052, China
Abstract
The automotive Ethernet is gradually replacing the traditional controller area network (CAN) as the backbone network of the vehicle. As an essential protocol to solve service-based communication, Scalable service-Oriented MiddlewarE over IP (SOME/IP) is expected to be applied to an in-vehicle network (IVN). The increasing number of external attack interfaces and the protocol’s vulnerability makes SOME/IP in-vehicle networks vulnerable to intrusion. This paper proposes a multi-layer intrusion detection system (IDS) architecture, including rule-based and artificial intelligence (AI)-based modules. The rule-based module is used to detect the SOME/IP header, SOME/IP-SD message, message interval, and communication process. The AI-based module acts on the payload. We propose a SOME/IP dataset establishment method to evaluate the performance of the proposed multi-layer IDS. Experiments are carried out on a Jetson Xavier NX, showing that the accuracy of AI-based detection reached 99.7761% and that of rule-based detection was 100%. The average detection time per packet is 0.3958 ms with graphics processing unit (GPU) acceleration and 0.6669 ms with only a central processing unit (CPU). After vehicle-level real-time analyses, the proposed IDS can be deployed for distributed or select critical advanced driving assistance system (ADAS) traffic for detection in a centralized layout.
Funder
Tongji University
Shanghai Pudong New Area Science and Technology Development Fund Industry-University-Research Special Project
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Cited by
6 articles.
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