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GSIDroid:一个可疑的子图驱动和可解释的Android恶意软件检测系统.

Hong Huang1, Weitao Huang1, Feng Jiang2

  • 1School of Computer Science and Engineering, Sichuan University of Science & Engineering, Zigong 643000, China.

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|July 12, 2025
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概括
此摘要是机器生成的。

GSIDroid通过子图分析和可解释性增强了Android恶意软件检测. 这种机器学习方法提高了安全性,并帮助网络安全专业人员了解恶意行为.

关键词:
安卓恶意软件检测检测 安卓恶意软件检测可以解释的机器学习图表 卷积网络 卷积网络语义信息是一种语义信息.

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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
  • 机器学习 机器学习

背景情况:

  • 安卓恶意软件带来了重大的经济和安全风险.
  • 现有的机器学习方法往往缺乏子图分析和可解释性.
  • 基于图形的检测方法可能是计算密集型,忽视节点语义.

研究的目的:

  • 介绍GSIDroid,这是一个用于子图驱动和可解释的Android恶意软件检测的新框架.
  • 为了提高检测性能,减少计算开销,并提高用户安全性.
  • 为网络安全专业人员提供透明和可分析的检测结果.

主要方法:

  • 开发了一个子图驱动的框架 (GSIDroid) 用于Android恶意软件检测.
  • 集成的深层和浅层语义功能与权限信息.
  • 集成的全球和本地解释性模块用于透明的分析.

主要成果:

  • 在14520个样本中,GSIDroid获得了97.14%的F1得分.
  • 解释模块成功识别了关键节点和权限特性.
  • 证明了增强的检测性能和减少的计算开销.

结论:

  • GSIDroid为Android恶意软件检测提供了一个有效和可解释的解决方案.
  • 该框架帮助网络安全专家进行严格的恶意软件分析和了解恶意行为.
  • GSIDroid 增强了实际部署,并支持进一步的安全研究.