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Updated: Dec 14, 2025

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Hierarchical Anomaly Detection Model for In-Vehicle Networks Using Machine Learning Algorithms.

Seunghyun Park1, Jin-Young Choi1

  • 1School of Cybersecurity, Korea University, Seoul 02841, Korea.

Sensors (Basel, Switzerland)
|July 19, 2020
PubMed
Summary

This study introduces a multi-labeled hierarchical classification (MLHC) intrusion detection model to combat vehicle cyberattacks. The MLHC model accurately detects and classifies message injection attacks, enhancing vehicle security.

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Area of Science:

  • Cybersecurity
  • Automotive Engineering
  • Network Security

Background:

  • Modern vehicles' increasing connectivity makes them vulnerable to cyberattacks.
  • Controller Area Network (CAN) alone is insufficient for protecting against external threats.
  • Passenger safety is jeopardized by system failures due to external attacks.

Purpose of the Study:

  • To propose a novel intrusion detection model for vehicles.
  • To detect and classify external attacks, specifically message injection.
  • To enhance the security and safety of connected vehicles.

Main Methods:

  • Development of a multi-labeled hierarchical classification (MLHC) intrusion detection model.
  • Utilizing existing classified attack data for analysis and classification.
Keywords:
MLHCanomaly detectioncontroller area networkhierarchical approachin-vehicle network securityintrusion detection systemmachine learning

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  • Comparative performance evaluation against Two Layers Multi-Class Detection (TLMD) and Single-Layer Multi-Class Classification (SLMC) models.

    • Main Results:

    • The MLHC model achieved a high F1 score of 0.9995.
    • MLHC demonstrated significantly faster detection times: 87.30% faster than SLMC and 99.92% faster than TLMD.
    • The model accurately classifies both the presence/absence and type of attacks.

    Conclusions:

    • The proposed MLHC model offers a highly accurate and efficient solution for detecting and classifying vehicle cyberattacks.
    • MLHC is suitable for high-throughput, in-vehicle communication environments.
    • This model enhances passenger safety by mitigating risks from external cyber threats.