Self-Supervised Anomaly Detection for In-Vehicle Network Using Noised Pseudo Normal Data

Hyun Min Song, Huy Kang Kim

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)


As the risk of cyber and safety threats to vehicle systems has increased, the anomaly detection in in-vehicle networks (IVN) has received the attention of researchers. Although, machine-learning-based anomaly detection methods have been proposed, there are limitations in detecting unknown attacks that the model has not learned because general supervised learning-based approaches depend on training dataset. To solve this problem, we propose a novel self-supervised method for IVN anomaly detection using noised pseudo normal data. The proposed method consists of two deep-learning models of the generator and the detector, which generates noised pseudo normal data and detects anomalies, respectively. Firstly, the generator is trained with only normal network traffic to generate pseudo normal traffic data. Then, the anomaly detector is trained to classify normal traffic and noised pseudo normal traffic as normal and abnormal, respectively. The experimental results demonstrate that the anomaly detection models, trained with the proposed method, not only significantly improved in the detection of unknown attacks, but also outperformed other semi-supervised learning-based methods.

Original languageEnglish
Article number9320546
Pages (from-to)1098-1108
Number of pages11
JournalIEEE Transactions on Vehicular Technology
Issue number2
Publication statusPublished - 2021 Feb

Bibliographical note

Publisher Copyright:
© 1967-2012 IEEE.


  • Anomaly detection
  • automotive security
  • controller area network
  • self-supervised learning

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics


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