Convolutional Neural Network-based Intrusion Detection System for AVTP Streams in Automotive Ethernet-based Networks

• URL: http://arxiv.org/abs/2102.03546v1
• Date: Sat, 6 Feb 2021 09:37:09 GMT
• Title: Convolutional Neural Network-based Intrusion Detection System for AVTP Streams in Automotive Ethernet-based Networks
• Authors: Seonghoon Jeong, Boosun Jeon, Boheung Chung, Huy Kang Kim
• Abstract summary: Connected and autonomous vehicles (CAVs) are an innovative form of traditional vehicles. No previous studies have focused on intrusion detection in automotive Ethernet-based networks. We present an intrusion detection method for detecting audio-video transport protocol (AVTP) stream injection attacks.
• Score: 2.141079906482723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Connected and autonomous vehicles (CAVs) are an innovative form of traditional vehicles. Automotive Ethernet replaces the controller area network and FlexRay to support the large throughput required by high-definition applications. As CAVs have numerous functions, they exhibit a large attack surface and an increased vulnerability to attacks. However, no previous studies have focused on intrusion detection in automotive Ethernet-based networks. In this paper, we present an intrusion detection method for detecting audio-video transport protocol (AVTP) stream injection attacks in automotive Ethernet-based networks. To the best of our knowledge, this is the first such method developed for automotive Ethernet. The proposed intrusion detection model is based on feature generation and a convolutional neural network (CNN). To evaluate our intrusion detection system, we built a physical BroadR-Reach-based testbed and captured real AVTP packets. The experimental results show that the model exhibits outstanding performance: the F1-score and recall are greater than 0.9704 and 0.9949, respectively. In terms of the inference time per input and the generation intervals of AVTP traffic, our CNN model can readily be employed for real-time detection.

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