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

arXiv: https://arxiv.org/abs/2108.08262 · HTML: https://mlsanity.com/paper/2108.08262/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 4 Aug 2021.

## Builds on (most cited first, 2 of 2)

- [GIDS: GAN based Intrusion Detection System for In-Vehicle Network](https://mlsanity.com/paper/1907.07377/) (2019)
- [Convolutional Neural Network-based Intrusion Detection System for AVTP Streams in Automotive Ethernet-based Networks](https://mlsanity.com/paper/2102.03546/) (2021)

## Built on by (most cited first, 3 of 3)

- [SISSA: Real-time Monitoring of Hardware Functional Safety and Cybersecurity with In-vehicle SOME/IP Ethernet Traffic](https://mlsanity.com/paper/2402.14862/) (2024)
- [Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks](https://mlsanity.com/paper/2202.00045/) (2022)
- [MDHP-Net: Detecting an Emerging Time-exciting Threat in IVN](https://mlsanity.com/paper/2411.10258/) (2024)

## Abstract (arXiv)

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.