  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.
