SOME/IP Intrusion Detection using Deep Learning-based Sequential Models in Automotive Ethernet Networks
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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 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 simple recurrent neural network (RNN), as an instance of deep learning-based sequential model, that we apply to our generated dataset. The numerical results show that RNN excel at predicting in-vehicle intrusions, with F1 Scores and AUC values of 0.99 for each type of intrusion.
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