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MALITE:用于受限制设备的轻量级恶意软件检测和分类.

Sidharth Anand1, Barsha Mitra2, Soumyadeep Dey3

  • 1University of California, San Diego, USA.

IEEE transactions on emerging topics in computing
|October 17, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了 MALITE,这是一款用于资源有限的设备的轻量级恶意软件分析系统. MALITE使用最小的内存和电池功率准确检测和分类恶意软件,优于现有方法.

关键词:
有限制的环境.轻量化 轻量化 轻量化 轻量化 轻量化恶意软件的分类 恶意软件的分类恶意软件检测检测 恶意软件检测

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科学领域:

  • 网络安全 网络安全
  • 机器学习 机器学习
  • 计算机法医学 计算机法医学

背景情况:

  • 恶意软件对所有计算设备构成重大威胁,尤其是对物联网设备等资源有限的设备.
  • 现有的机器学习恶意软件分析方法通常对这些环境来说过于资源密集.

研究的目的:

  • 开发适用于资源有限的设备的轻量级恶意软件分析系统 (MALITE).
  • 准确区分良性二进制文件和恶意文件,并对恶意软件家族进行分类.

主要方法:

  • MALITE将二进制数据转换为图像 (灰度或RGB) 用于分析.
  • 它采用了两个新的轻量级架构:MALITE-MN (神经网络) 和MALITE-HRF (具有直方图特征的随机森林).

主要成果:

  • MALITE-MN和MALITE-HRF在恶意软件识别和分类方面表现出高准确度.
  • 与最先进的基线相比,这两种方法都显著降低了内存和计算资源的消耗.

结论:

  • MALITE提供了一种有效且资源高效的解决方案,用于在受限制设备上进行恶意软件分析.
  • 该系统的低资源需求使其成为移动和物联网安全应用的理想选择.