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Updated: Jan 7, 2026

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MaSS-Droid:使用多层功能选和堆叠集成的Android恶意软件检测框架.

Zihao Zhang1,2, Qiang Han1,2, Zhichao Shi1,2

  • 1School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China.

Entropy (Basel, Switzerland)
|December 24, 2025
PubMed
概括
此摘要是机器生成的。

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本研究介绍了MaSS-Droid,这是一个新的Android恶意软件检测框架. 它通过减少特征冗余和提高复杂威胁的模型稳定性和准确性来增强安全性.

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:

  • 安卓恶意软件是一个日益增长的威胁,挑战用户安全.
  • 恶意软件检测面临特征冗余和不稳定的单个模型性能问题.

研究的目的:

  • 提出MaSS-Droid,这是一个强大的Android恶意软件检测框架.
  • 解决特征冗余问题,提高组合模型的概括性和稳定性.

主要方法:

  • 从APK文件中提取权限,API调用和opcode序列功能.
  • 实施了三层功能选机制,以减少冗余和复杂性.
  • 使用自适应堆叠集成方法 (自适应堆叠) 来动态调整基础分类器权重.

主要成果:

  • MaSS-Droid有效地减轻了过拟合,并增强了模型的概括性.
  • 该框架显著减少了特征冗余,提高了检测准确度.
  • 在检测各种安卓恶意软件样本时,证明了整体稳定性和准确性.

结论:

  • MaSS-Droid为Android恶意软件检测提供了一个稳定而准确的解决方案.
关键词:
安卓恶意软件检测检测 安卓恶意软件检测堆叠集成的整合方式功能选择 功能选择静态分析 静态分析

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  • 拟议的框架有效地解决了特征冗余和模型不稳定性所带来的挑战.
  • 适应堆叠集成证明在恶意软件分析中卓越的合奏性能至关重要.