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Vision01:24

Vision

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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HiViT-IDS: An Efficient Network Intrusion Detection Method Based on Vision Transformer.

Hai Zhou1, Haojie Zou1, Wei Li1

  • 1College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China.

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|April 28, 2025
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Summary

This study introduces the High-performance ViT Intrusion Detection System (HiViT-IDS) for enhanced Internet of Things (IoT) security. The novel approach significantly reduces training time while maintaining high accuracy in detecting network intrusions.

Keywords:
deep learningnetwork intrusion detectionvision transformer

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Area of Science:

  • Cybersecurity
  • Network Security
  • Machine Learning

Background:

  • Internet of Things (IoT) systems face increasing security threats due to widespread adoption in critical sectors.
  • Traditional Intrusion Detection Systems (IDS) using Machine Learning (ML) struggle with classifying complex, dynamic malicious traffic.
  • Deep Transfer Learning (DTL) shows promise but often incurs high training time and resource costs.

Purpose of the Study:

  • To develop an efficient Intrusion Detection System (IDS) that balances high accuracy with reduced training time for IoT environments.
  • To leverage the Vision Transformer (ViT) model's feature extraction capabilities for improved network traffic classification.
  • To introduce the High-performance ViT Intrusion Detection System (HiViT-IDS) as a solution for dynamic security challenges.

Main Methods:

  • Network traffic data is converted from one-dimensional streams into RGB images.
  • The Vision Transformer (ViT) model is employed for its powerful feature extraction and classification capabilities.
  • The proposed HiViT-IDS model is evaluated on the ToN-IoT and Edge-IIoTset datasets.

Main Results:

  • The HiViT-IDS achieved classification accuracies of 99.70% on the ToN-IoT dataset and 100% on the Edge-IIoTset dataset.
  • Compared to existing Deep Transfer Learning (DTL) methods, HiViT-IDS demonstrated substantial reductions in training time.
  • The model effectively sustains high performance metrics while optimizing computational resource utilization.

Conclusions:

  • The HiViT-IDS offers a highly efficient and accurate solution for detecting intrusions in complex IoT networks.
  • The Vision Transformer (ViT) architecture proves effective in enhancing network security by converting traffic data into image representations.
  • HiViT-IDS presents a competitive advantage for adapting to evolving and dynamic network security environments.