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相关实验视频

Updated: Sep 11, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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一个可解释的混合CNN-变压器架构用于视觉恶意软件分类.

Mohammed Alshomrani1, Aiiad Albeshri1, Abdulaziz A Alsulami2

  • 1Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Sensors (Basel, Switzerland)
|August 14, 2025
PubMed
概括
此摘要是机器生成的。

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EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
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结合ConvNeXt-Tiny和Swin Transformer的新混合深度学习模型在视觉恶意软件分类中实现了94.04%的准确性. 这种方法为检测不断演变的恶意代码提供了有效和可解释的解决方案.

科学领域:

  • 网络安全 网络安全
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 传统的基于签名的恶意软件检测与不断变化的威胁作斗争.
  • 视觉恶意软件分类,将二进制文件转换为图像,提供了一个有希望的替代方案.
  • 像CNN和Transformers这样的深度学习模型显示出基于图像的恶意软件分析的潜力.

研究的目的:

  • 开发和评估一种混合深度学习模型,用于增强视觉恶意软件分类.
  • 结合卷积神经网络 (CNN) 和变压器的优势,以改善模式识别.
  • 评估模型的性能,可解释性和实时适用性.

主要方法:

  • 这是一个混合深度学习架构,集成ConvNeXt-Tiny (CNN) 和Swin Transformer.
  • 在基准数据集上的培训和验证:Malimg,MaleVis,VirusMNIST (61类).
  • 评估使用额外的数据集 (Maldeb,Dumpware-10) 和梯度加权类激活映射 (Grad-CAM) 进行解释性.

主要成果:

  • 混合型号实现了94.04%的验证精度,超过了单个的ConvNeXt-Tiny (92.45%) 和Swin Transformer (90.44%) 模型.
  • 在扩展数据集上实现了高精度:在Maldeb上98%和在Dumpware-10上97%的精度.
关键词:
这是Grad-CAM.一个常见的下一个.网络安全 网络安全深度学习是一种深度学习.可以解释的人工智能AI恶意软件的分类恶意软件的分类视觉变压器 视觉变压器

相关实验视频

Last Updated: Sep 11, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.0K
  • 格拉德-CAM可视化证实了CNN和变压器组件的互补特征提取.

    • 结论:

    • 混合深度学习方法为视觉恶意软件分类提供了强大的和可解释的方法.
    • 该模型通过实时部署场景来证明其实际适用性.
    • 这项研究有助于更有效和可靠的自动化恶意软件检测系统.