# Detecting In-vehicle Intrusion via Semi-supervised Learning-based Convolutional Adversarial Autoencoders

arXiv: https://arxiv.org/abs/2204.01193 · HTML: https://mlsanity.com/paper/2204.01193/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2022.

## Built on by (most cited first, 7 of 7)

- [Evaluating lightweight unsupervised online IDS for masquerade attacks in CAN](https://mlsanity.com/paper/2406.13778/) (2024)
- [Supervised Contrastive ResNet and Transfer Learning for the In-vehicle Intrusion Detection System](https://mlsanity.com/paper/2207.10814/) (2022)
- [STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph Learning](https://mlsanity.com/paper/2311.07056/) (2023)
- [A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network](https://mlsanity.com/paper/2505.11551/) (2025)
- [Alleviating Attack Data Scarcity: SCANIA's Experience Towards Enhancing In-Vehicle Cyber Security Measures](https://mlsanity.com/paper/2507.02607/) (2025)
- [MIDS: Detecting Stealthy Masquerade and Tampering Attacks on CAN Bus via Bidirectional Mamba](https://mlsanity.com/paper/2606.18599/) (2026)
- [Temporal transformer CAN encoder with federated lightweight heads for anomaly detection](https://mlsanity.com/paper/2610.10613/) (2026)

## Abstract (arXiv)

With the development of autonomous vehicle technology, the controller area network (CAN) bus has become the de facto standard for an in-vehicle communication system because of its simplicity and efficiency. However, without any encryption and authentication mechanisms, the in-vehicle network using the CAN protocol is susceptible to a wide range of attacks. Many studies, which are mostly based on machine learning, have proposed installing an intrusion detection system (IDS) for anomaly detection in the CAN bus system. Although machine learning methods have many advantages for IDS, previous models usually require a large amount of labeled data, which results in high time and labor costs. To handle this problem, we propose a novel semi-supervised learning-based convolutional adversarial autoencoder model in this paper. The proposed model combines two popular deep learning models: autoencoder and generative adversarial networks. First, the model is trained with unlabeled data to learn the manifolds of normal and attack patterns. Then, only a small number of labeled samples are used in supervised training. The proposed model can detect various kinds of message injection attacks, such as DoS, fuzzy, and spoofing, as well as unknown attacks. The experimental results show that the proposed model achieves the highest F1 score of 0.99 and a low error rate of 0.1% with limited labeled data compared to other supervised methods. In addition, we show that the model can meet the real-time requirement by analyzing the model complexity in terms of the number of trainable parameters and inference time. This study successfully reduced the number of model parameters by five times and the inference time by eight times, compared to a state-of-the-art model.