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  1. 首页
  2. 使用卷积神经网络 (cnn) 与转移学习检测恶意代码变体.
  1. 首页
  2. 使用卷积神经网络 (cnn) 与转移学习检测恶意代码变体.

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使用卷积神经网络 (CNN) 与转移学习检测恶意代码变体.

Nazish Younas1, Shazia Riaz2,3, Saqib Ali1,4

  • 1Department of Computer Science, University of Agriculture Faisalabad, Faisalabad, Pakistan.

PeerJ. Computer science
|June 26, 2025

在PubMed 上查看摘要

概括
此摘要是机器生成的。

这项研究引入了一个新的恶意软件变体检测系统 (MVDS),该系统将恶意代码转换为彩色图像,以提高检测能力. 该系统达到97.98%的准确性,为网络安全提供了更快,更有效的方法.

关键词:
在美国,CNN是CNN.恶意代码变体的恶意代码变体恶意软件变体检测系统系统转移学习转移学习视觉化的可视化

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科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 网络安全 网络安全

背景情况:

  • 恶意软件对数字系统构成重大威胁,需要先进的检测方法.
  • 当前的反恶意软件解决方案和检测技术往往在效率和准确性方面扎.
  • 现有的基于图像的恶意软件检测使用灰度图像是计算密集的.

研究的目的:

  • 开发一种新且高效的恶意软件检测系统.
  • 为了提高恶意软件变种分类的准确性.
  • 为了克服灰度图像转换用于恶意软件分析的局限性.

主要方法:

  • 提出了恶意软件变种检测系统 (MVDS).
  • 将恶意代码转换为彩色图像进行分析.
  • 利用转移学习用于自动化恶意软件图像分类.

主要成果:

  • 获得了97.98%的分类准确率.
  • 与传统方法相比,证明了较高的检测速度.
  • 彩色图像的转换被证明比灰度更有效.

结论:

  • MVDS提供了增强的恶意软件检测功能.
  • 该系统的高精度和速度使其适用于实际的网络安全.
  • 利用彩色图像和转移学习显著改善恶意软件的识别.