mlsanityData to 7 Oct 2026

Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

arXiv 2107.05172 ↗First posted 12 Jul 2021cs.CR cs.AIcited by 10

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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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 6 of its 63 references that are in the map. First posted on arXiv 12 Jul 2021.

Builds on

6 of 63 references.

References · 6

  1. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  2. 2018Transfer learning for time series classificationcited by 60
  3. 2019GIDS: GAN based Intrusion Detection System for In-Vehicle Networkcited by 42
  4. 2018State-of-the-Art Survey on In-Vehicle Network Communication (CAN-Bus) Security and Vulnerabilitiescited by 7
  5. 2019Detection of Advanced Malware by Machine Learning Techniquescited by 5
  6. 2018Linking Received Packet to the Transmitter Through Physical-Fingerprinting of Controller Area Networkcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 3; 2025: 3; 2026: 1.

10 papers.

  1. 2023Deep transfer learning for intrusion detection in industrial control networks: A comprehensive reviewcited by 21
  2. 2022A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehiclescited by 11
  3. 2024Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AIcited by 4
  4. 2022Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Networkcited by 3
  5. 2024A Survey of Anomaly Detection in In-Vehicle Networkscited by 1
  6. 2024XAI-based Feature Selection for Improved Network Intrusion Detection Systemscited by 0
  7. 2025Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoTcited by 0
  8. 2025A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Networkcited by 0
  9. 2025Towards Ultra-Low Latency: Binarized Neural Network Architectures for In-Vehicle Network Intrusion Detectioncited by 0
  10. 2026DAIRE: A lightweight AI model for real-time detection of Controller Area Network attacks in the Internet of Vehiclescited by 0