  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 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 greater than 0.8 depending on each
intrusion type.
