SOME/IP Intrusion Detection using Deep Learning-based Sequential Models in Automotive Ethernet Networks

Intrusion Detection Systems are widely used to detect cyberattacks, especially on protocols vulnerable to hacking attacks such as SOME/IP. In this paper, we present a deep learning-based sequential model for offline intrusion detection on SOME/IP application layer protocol. To assess our intrusion detection system, we have generated and labeled a dataset 1 with several classes representing realistic intrusions, and a normal class-a significant contribution due to the absence of such publicly available datasets. Furthermore, we also propose a recurrent neural network (RNN), as an instance of deep learningbased sequential model, that we apply to our generated dataset. The numerical results show that RNN excel at predicting invehicle intrusions, with F1 Scores and AUC values greater than 0.8 depending on each intrusion type.