# Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

arXiv: https://arxiv.org/abs/2107.05172 · HTML: https://mlsanity.com/paper/2107.05172/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 63 references that are in the map.
- First posted on arXiv 12 Jul 2021.

## Builds on (most cited first, 6 of 6)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Transfer learning for time series classification](https://mlsanity.com/paper/1811.01533/) (2018)
- [GIDS: GAN based Intrusion Detection System for In-Vehicle Network](https://mlsanity.com/paper/1907.07377/) (2019)
- [State-of-the-Art Survey on In-Vehicle Network Communication (CAN-Bus) Security and Vulnerabilities](https://mlsanity.com/paper/1802.01725/) (2018)
- [Detection of Advanced Malware by Machine Learning Techniques](https://mlsanity.com/paper/1903.02966/) (2019)
- [Linking Received Packet to the Transmitter Through Physical-Fingerprinting of Controller Area Network](https://mlsanity.com/paper/1801.09011/) (2018)

## Built on by (most cited first, 10 of 10)

- [Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review](https://mlsanity.com/paper/2304.10550/) (2023)
- [A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles](https://mlsanity.com/paper/2201.11812/) (2022)
- [Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI](https://mlsanity.com/paper/2410.09043/) (2024)
- [Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network](https://mlsanity.com/paper/2207.10286/) (2022)
- [A Survey of Anomaly Detection in In-Vehicle Networks](https://mlsanity.com/paper/2409.07505/) (2024)
- [XAI-based Feature Selection for Improved Network Intrusion Detection Systems](https://mlsanity.com/paper/2410.10050/) (2024)
- [Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT](https://mlsanity.com/paper/2502.06099/) (2025)
- [A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network](https://mlsanity.com/paper/2505.11551/) (2025)
- [Towards Ultra-Low Latency: Binarized Neural Network Architectures for In-Vehicle Network Intrusion Detection](https://mlsanity.com/paper/2511.00828/) (2025)
- [DAIRE: A lightweight AI model for real-time detection of Controller Area Network attacks in the Internet of Vehicles](https://mlsanity.com/paper/2604.20771/) (2026)

## Abstract (arXiv)

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.