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

Natasha Alkhatib 1, 2, 3 Jean-Luc Danger 1, 2, 3 Hadi Ghauch 1, 4, 3
2 SSH - Secure and Safe Hardware
LTCI - Laboratoire Traitement et Communication de l'Information
4 COMNUM - Communications Numériques
LTCI - Laboratoire Traitement et Communication de l'Information
Abstract : 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.
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https://hal.archives-ouvertes.fr/hal-03372353
Contributor : Natasha Alkhatib Connect in order to contact the contributor
Submitted on : Sunday, October 10, 2021 - 3:49:22 PM
Last modification on : Thursday, November 18, 2021 - 1:02:02 PM

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  • HAL Id : hal-03372353, version 1

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Natasha Alkhatib, Jean-Luc Danger, Hadi Ghauch. SOME/IP Intrusion Detection using Deep Learning-based Sequential Models in Automotive Ethernet Networks. IEEE IEMCON 2021, Oct 2021, Vancouver, Canada. ⟨hal-03372353⟩

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