# A Survey of Anomaly Detection in In-Vehicle Networks

arXiv: https://arxiv.org/abs/2409.07505 · HTML: https://mlsanity.com/paper/2409.07505/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 143 references that are in the map.
- First posted on arXiv 11 Sep 2024.

## Builds on (most cited first, 17 of 17)

- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [A review on outlier/anomaly detection in time series data](https://mlsanity.com/paper/2002.04236/) (2020)
- [MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks](https://mlsanity.com/paper/1901.04997/) (2019)
- [D"IoT: A Federated Self-learning Anomaly Detection System for IoT](https://mlsanity.com/paper/1804.07474/) (2018)
- [GIDS: GAN based Intrusion Detection System for In-Vehicle Network](https://mlsanity.com/paper/1907.07377/) (2019)
- [Learning from Irregularly-Sampled Time Series: A Missing Data Perspective](https://mlsanity.com/paper/2008.07599/) (2020)
- [Federated Learning for Malware Detection in IoT Devices](https://mlsanity.com/paper/2104.09994/) (2021)
- [CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data](https://mlsanity.com/paper/1906.02492/) (2019)
- [Conformal k-NN Anomaly Detector for Univariate Data Streams](https://mlsanity.com/paper/1706.03412/) (2017)
- [INDRA: Intrusion Detection using Recurrent Autoencoders in Automotive Embedded Systems](https://mlsanity.com/paper/2007.08795/) (2020)
- [Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks](https://mlsanity.com/paper/2107.05172/) (2021)
- [LATTE: LSTM Self-Attention based Anomaly Detection in Embedded Automotive Platforms](https://mlsanity.com/paper/2107.05561/) (2021)
- [Cyberattacks and Countermeasures For In-Vehicle Networks](https://mlsanity.com/paper/2004.10781/) (2020)
- [Detection of Message Injection Attacks onto the CAN Bus using Similarity of Successive Messages-Sequence Graphs](https://mlsanity.com/paper/2104.03763/) (2021)
- [X-CANIDS: Signal-Aware Explainable Intrusion Detection System for Controller Area Network-Based In-Vehicle Network](https://mlsanity.com/paper/2303.12278/) (2023)
- [Semi-Markov Switching Vector Autoregressive Model-based Anomaly Detection in Aviation Systems](https://mlsanity.com/paper/1602.06550/) (2016)
- [CASAD: CAN-Aware Stealthy-Attack Detection for In-Vehicle Networks](https://mlsanity.com/paper/1909.08407/) (2019)

## Built on by (most cited first, 2 of 2)

- [CAN-QA: A Question-Answering Benchmark for Reasoning over In-Vehicle CAN Traffic](https://mlsanity.com/paper/2604.24935/) (2026)
- [Temporal transformer CAN encoder with federated lightweight heads for anomaly detection](https://mlsanity.com/paper/2610.10613/) (2026)

## Abstract (arXiv)

Modern vehicles are equipped with Electronic Control Units (ECU) that are used for controlling important vehicle functions including safety-critical operations. ECUs exchange information via in-vehicle communication buses, of which the Controller Area Network (CAN bus) is by far the most widespread representative. Problems that may occur in the vehicle's physical parts or malicious attacks may cause anomalies in the CAN traffic, impairing the correct vehicle operation. Therefore, the detection of such anomalies is vital for vehicle safety. This paper reviews the research on anomaly detection for in-vehicle networks, more specifically for the CAN bus. Our main focus is the evaluation of methods used for CAN bus anomaly detection together with the datasets used in such analysis. To provide the reader with a more comprehensive understanding of the subject, we first give a brief review of related studies on time series-based anomaly detection. Then, we conduct an extensive survey of recent deep learning-based techniques as well as conventional techniques for CAN bus anomaly detection. Our comprehensive analysis delves into anomaly detection algorithms employed in in-vehicle networks, specifically focusing on their learning paradigms, inherent strengths, and weaknesses, as well as their efficacy when applied to CAN bus datasets. Lastly, we highlight challenges and open research problems in CAN bus anomaly detection.