# Convolutional Neural Network-based Intrusion Detection System for AVTP Streams in Automotive Ethernet-based Networks

arXiv: https://arxiv.org/abs/2102.03546 · HTML: https://mlsanity.com/paper/2102.03546/

## Key facts

- Cited by 5 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 6 Feb 2021.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)

## Built on by (most cited first, 5 of 5)

- [GGNB: Graph-Based Gaussian Naive Bayes Intrusion Detection System for CAN Bus](https://mlsanity.com/paper/2108.10908/) (2021)
- [SOME/IP Intrusion Detection using Deep Learning-based Sequential Models in Automotive Ethernet Networks](https://mlsanity.com/paper/2108.08262/) (2021)
- [Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks](https://mlsanity.com/paper/2202.00045/) (2022)
- [Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems](https://mlsanity.com/paper/2207.04843/) (2022)
- [Deep Learning-Based Intrusion Detection for Automotive Ethernet: Evaluating & Optimizing Fast Inference Techniques for Deployment on Low-Cost Platform](https://mlsanity.com/paper/2507.01208/) (2025)

## Abstract (arXiv)

Connected and autonomous vehicles (CAVs) are an innovative form of traditional vehicles. Automotive Ethernet replaces the controller area network and FlexRay to support the large throughput required by high-definition applications. As CAVs have numerous functions, they exhibit a large attack surface and an increased vulnerability to attacks. However, no previous studies have focused on intrusion detection in automotive Ethernet-based networks. In this paper, we present an intrusion detection method for detecting audio-video transport protocol (AVTP) stream injection attacks in automotive Ethernet-based networks. To the best of our knowledge, this is the first such method developed for automotive Ethernet. The proposed intrusion detection model is based on feature generation and a convolutional neural network (CNN). To evaluate our intrusion detection system, we built a physical BroadR-Reach-based testbed and captured real AVTP packets. The experimental results show that the model exhibits outstanding performance: the F1-score and recall are greater than 0.9704 and 0.9949, respectively. In terms of the inference time per input and the generation intervals of AVTP traffic, our CNN model can readily be employed for real-time detection.