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A lightweight intrusion detection system for internet of vehicles based on transfer learning and MobileNetV2 with hyper-parameter optimization

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Abstract

With the rapid development of Internet of Vehicles (IoV) technology, Intelligent Connected Vehicles (ICVs) have richer vehicle information functions and applications. In recent years, as ICVs have become more complex and intelligent, vehicle information security is facing great threats and challenges. Therefore, it is of great significance to develop efficient intrusion detection methods to protect the information security of IoV. In this paper, after analyzing the vulnerability of intra-vehicle networks (IVNs) and external vehicle networks (EVNs), we propose a lightweight intrusion detection method, which uses MobileNetv2 as the backbone, combines transfer learning (TL) techniques and the hyper-parameter optimization (HPO) method. The proposed method can detect various types of attacks, and the Accuracy, Precision, and Recall on the Car-Hacking dataset representing IVNs data are all 100 \(\mathrm{\%}\). The Accuracy, Precision, and Recall on the CICIDS2017 dataset representing EVNs data are all 99.93 \(\mathrm{\%}\). The average processing time of each packet tested is about 0.75 ms, and the model space is 23 M. Experimental results demonstrate that the proposed intrusion detection method is effective and lightweight.

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Data availability

All data can be obtained by contacting the first author.

Notes

  1. The complete Car-Hacking dataset is publicly available at: https://ocslab.hksecurity.net/Datasets/CAN-intrusion-dataset

  2. The CICIDS2017 dataset is publicly available at: https://www.unb.ca/cic/datasets/ids-2017.html

  3. code for the major modules is available at: https://github.com/cailv/demo

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Funding

This work was supported in part by the Jilin Scientific and Technological Development Program under Grant (20200401132GX and 20210101166JC).

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Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Yingqing Wang, Guihe Qin, Mi Zou, Yanhua Liang, Guofeng Wang, Kunpeng Wang, Yao Feng & Zizhan Zhang

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Yingqing Wang, Guihe Qin, Mi Zou, Yanhua Liang, Guofeng Wang, Kunpeng Wang, Yao Feng & Zizhan Zhang

  3. Public Computer Education and Research Center, Jilin University, Changchun, 130012, China

    Mi Zou

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  1. Yingqing Wang
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Correspondence to Mi Zou or Yanhua Liang.

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Wang, Y., Qin, G., Zou, M. et al. A lightweight intrusion detection system for internet of vehicles based on transfer learning and MobileNetV2 with hyper-parameter optimization. Multimed Tools Appl 83, 22347–22369 (2024). https://doi.org/10.1007/s11042-023-15771-6

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