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
概括
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.
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.
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