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LCSMC-Net: Lightweight CAN Intrusion Detection via Separable Multiscale Convolution and Attention.

Mengdi Hou1,2, Bitie Lan1, Chenghua Tang2

  • 1School of Electronic Information and Artificial Intelligence, Wuzhou University, Wuzhou 543000, China.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
Summary

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This study introduces LCSMC-Net, a novel, ultra-lightweight neural network for detecting intrusions in Controller Area Network (CAN) systems. It offers high accuracy with minimal computational resources, enabling secure vehicle networks.

Area of Science:

  • Automotive Cybersecurity
  • Embedded Systems AI
  • Deep Learning for Network Security

Background:

  • Controller Area Network (CAN) protocol lacks inherent security, making vehicles vulnerable to cyberattacks.
  • Existing deep learning intrusion detection systems are too computationally intensive for automotive microcontrollers.
  • There is a critical need for efficient, embedded security solutions for in-vehicle networks.

Purpose of the Study:

  • To propose LCSMC-Net, an ultra-lightweight neural network architecture for intrusion detection in resource-constrained CAN environments.
  • To develop a practical and efficient edge AI solution for automotive cybersecurity.
  • To address the limitations of current deep learning models in embedded automotive systems.

Main Methods:

  • Developed Separable Multiscale Convolution Lite (SMC-Lite) blocks for efficient feature extraction.
Keywords:
CAN bus securityattention mechanismautomotive cybersecurityedge AIembedded systemsintrusion detectionknowledge distillationlightweight neural networks

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  • Implemented Lightweight Channel-Temporal Attention (LCTA) with linear complexity for adaptive pruning.
  • Utilized 6-dimensional CAN-optimized features for aggressive data compression.
  • Employed Bayesian hyperparameter optimization and knowledge distillation for model compression.

    • Main Results:

    • LCSMC-Net achieved 99.89% accuracy on CAN and CAN-FD datasets.
    • The model has only 9401 parameters and 2.84M FLOPs, significantly reducing computational load.
    • Outperformed existing intrusion detection solutions in terms of efficiency and accuracy.
    • Demonstrated feasibility for real-time deployment on automotive-grade microcontrollers.

    Conclusions:

    • LCSMC-Net provides a viable edge AI solution for securing CAN networks against intrusions.
    • The proposed architecture meets the stringent real-time and resource constraints of automotive embedded systems.
    • This research enables practical deployment of advanced AI-driven security in modern vehicles.