在当代背景下重新评估基于功能的Android恶意软件检测
Ali Muzaffar1, Hani Ragab Hassen1, Hind Zantout1
1Heriot-Watt University, Dubai, United Arab Emirates.
PloS one
|January 20, 2026
概括
针对Android恶意软件检测的基于功能的机器学习仍然实现了超过98%的准确性. 简单的模型和像API调用这样的静态分析功能是有效的,挑战了对复杂方法的趋势.
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:
- 安卓恶意软件检测严重依赖于机器学习.
- 之前的研究探讨了各种特征集和模型.
- 不断变化的Android环境需要对现有方法进行重新评估.
研究的目的:
- 重新实施和评估基础的基于功能的机器学习研究,用于Android恶意软件检测.
- 评估基于特征的方法在当代环境中的有效性.
- 为了比较静态与动态分析功能和简单与复杂模型的性能.
主要方法:
- 重新实施18项关于Android恶意软件检测的关键研究 (2013-2023年).
- 使用了124,000个应用程序的平衡数据集.
- 从静态和动态分析中评估的功能集,包括API调用,操作代码和网络流量.
- 简单和复杂的机器学习模型的性能比较,包括合集.
主要成果:
- 基于特征的方法实现了超过98%的检测准确度.
- 静态分析功能 (API调用,电话代码) 的效率很高.
- 动态分析功能 (网络流量) 显示中等效益.
- 简单的模型通常表现优于复杂的模型.
- 集合模型有效地结合了静态和动态特征.
结论:
- 基于特征的机器学习仍然是Android恶意软件检测的可行和有效策略.
- 简单,快速的模型与复杂的方法竞争,有时甚至优于复杂的方法.
- 静态分析功能至关重要,动态功能提供增量改进.
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