mlsanityData to 7 Oct 2026

A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles

arXiv 2201.11812 ↗First posted 27 Jan 2022cs.CR cs.AI cs.CVcited by 11

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Abstract (arXiv)

Modern vehicles, including autonomous vehicles and connected vehicles, are increasingly connected to the external world, which enables various functionalities and services. However, the improving connectivity also increases the attack surfaces of the Internet of Vehicles (IoV), causing its vulnerabilities to cyber-threats. Due to the lack of authentication and encryption procedures in vehicular networks, Intrusion Detection Systems (IDSs) are essential approaches to protect modern vehicle systems from network attacks. In this paper, a transfer learning and ensemble learning-based IDS is proposed for IoV systems using convolutional neural networks (CNNs) and hyper-parameter optimization techniques. In the experiments, the proposed IDS has demonstrated over 99.25% detection rates and F1-scores on two well-known public benchmark IoV security datasets: the Car-Hacking dataset and the CICIDS2017 dataset. This shows the effectiveness of the proposed IDS for cyber-attack detection in both intra-vehicle and external vehicular networks.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 14 of its 23 references that are in the map. First posted on arXiv 27 Jan 2022.

Builds on

14 of 23 references.

References · 14

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  3. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,320
  4. 2020On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practicecited by 181
  5. 2019GIDS: GAN based Intrusion Detection System for In-Vehicle Networkcited by 42
  6. 2021MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of Vehiclescited by 36
  7. 2021A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streamscited by 28
  8. 2019Tree-based Intelligent Intrusion Detection System in Internet of Vehiclescited by 25
  9. 2021PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streamscited by 17
  10. 2020Edge-enabled V2X Service Placement for Intelligent Transportation Systemscited by 11
  11. 2021Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networkscited by 10
  12. 2021Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detectioncited by 9
2 more
  1. 2019Measuring the Transferability of Adversarial Examplescited by 6
  2. 2020IoT DoS and DDoS Attack Detection using ResNetcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 1; 2024: 2; 2025: 4; 2026: 2.

11 papers.

  1. 2024On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Casecited by 9
  2. 2022LCCDE: A Decision-Based Ensemble Framework for Intrusion Detection in The Internet of Vehiclescited by 6
  3. 2025Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysiscited by 4
  4. 2024On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approachcited by 1
  5. 2025Toward Autonomous and Efficient Cybersecurity: A Multi-Objective AutoML-based Intrusion Detection Systemcited by 1
  6. 2022Anomaly Detection in Intra-Vehicle Networkscited by 0
  7. 2023Forensic Data Analytics for Anomaly Detection in Evolving Networkscited by 0
  8. 2025Adaptive Intrusion Detection for Evolving RPL IoT Attacks Using Incremental Learningcited by 0
  9. 2025FedLiTeCAN : A Federated Lightweight Transformer for Fast and Robust CAN Bus Intrusion Detectioncited by 0
  10. 2026Generative AI and Federated Learning for Intrusion Detection Systems: A Surveycited by 0
  11. 2026An LLM-Assisted AutoML Framework for Intrusion Detection in IoT Networkscited by 0