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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:基于视觉变压器的高效网络入侵检测方法

Hai Zhou1, Haojie Zou1, Wei Li1

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

Sensors (Basel, Switzerland)
|April 28, 2025
PubMed
概括

本研究介绍了用于增强物联网 (IoT) 安全性的高性能ViT入侵检测系统 (HiViT-IDS). 这种新的方法显著减少了培训时间,同时在检测网络入侵时保持了高准确度.

科学领域:

  • 网络安全 网络安全
  • 网络安全 网络安全
  • 机器学习 机器学习

背景情况:

  • 物联网 (IoT) 系统面临着越来越多的安全威胁,因为它在关键领域得到广泛采用.
  • 使用机器学习 (ML) 的传统入侵检测系统 (IDS) 难以对复杂,动态的恶意流量进行分类.
  • 深度转移学习 (DTL) 是有前途的,但往往需要花费大量的培训时间和资源.

研究的目的:

  • 为物联网环境开发一个高效的入侵检测系统 (IDS),以高精度与缩短训练时间进行平衡.
  • 为了利用视觉变压器 (ViT) 模型的特征提取能力来改进网络流量分类.
  • 引入高性能ViT入侵检测系统 (HiViT-IDS),作为应对动态安全挑战的解决方案.

主要方法:

  • 网络流量数据从一维流转换为RGB图像.
  • 视觉变压器 (ViT) 模型用于其强大的特征提取和分类功能.
  • 拟议的HiViT-IDS模型是根据ToN-IoT和Edge-IIoT数据集进行评估的.

主要成果:

  • HiViT-IDS在ToN-IoT数据集上实现了99.70%的分类准确率,在Edge-IIoTset数据集上达到100%.
  • 与现有的深度转移学习 (DTL) 方法相比,HiViT-IDS显示了培训时间的大幅减少.
关键词:
深度学习是一种深度学习.网络入侵检测检测 网络入侵检测视觉变压器 视觉变压器

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  • 该模型有效地维持高性能指标,同时优化计算资源利用率.

    • 结论:

    • HiViT-IDS为检测复杂物联网网络入侵提供了一个高效和准确的解决方案.
    • 视觉转换器 (ViT) 架构通过将流量数据转换为图像表示来提高网络安全的有效性.
    • 在适应不断变化的动态网络安全环境方面,HiViT-IDS具有竞争优势.