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PermDroid是一个使用建议的功能选择方法和机器学习技术开发的框架,用于Android恶意软件检测.

Arvind Mahindru1, Himani Arora2, Abhinav Kumar3

  • 1Department of Computer Science and applications, D.A.V. University, Sarmastpur, Jalandhar, 144012, India. er.arvindmahindru@gmail.com.

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概括
此摘要是机器生成的。

这项研究引入了一种新的功能选择框架,以改善Android恶意软件检测. 拟议的方法提高了机器学习模型的准确性,实现了98.8%的Android恶意软件检测.

关键词:
在 API 调用时,会调用 API 调用.安卓应用程序安卓应用程序深度学习是一种深度学习.功能选择 功能选择侵入检测入侵检测系统可以检测入侵.神经网络的神经网络许可证模型的模型.

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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:

  • 由于其许可模式,Android恶意软件检测具有挑战性.
  • 以前的方法经常使用过多的特征,导致模型过载.
  • 有效的机器学习依赖于相关的,有区别的特征.

研究的目的:

  • 为Android恶意软件检测提出一个功能选择框架.
  • 识别相关特征,以提高模型准确性和减少错误分类.
  • 开发和评估机器学习模型使用选定的功能.

主要方法:

  • 实施了两阶段的特征选择框架.
  • 第一个阶段:t测试和单变量逻辑回归.
  • 第二阶段:多变量线性回归和相关性分析;用合体方法和神经网络构建的模型.

主要成果:

  • 功能选择框架确定了用于恶意软件检测的相关功能.
  • 使用特定特征的模型表现优于使用所有提取特征的模型.
  • 开发的模型在50万个Android应用程序中实现了98.8%的高精度.

结论:

  • 拟议的功能选择框架有效地增强了Android恶意软件检测.
  • 优化的功能集导致更准确,更有效的机器学习模型.
  • 这种方法比现有的Android恶意软件检测方法有了显著的改进.