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使用MH-100K检测Android恶意软件:用于高级研究的创新数据集.

Hendrio Bragança1, Vanderson Rocha1, Lucas Barcellos2

  • 1Institute of Computing, Federal University of Amazonas, Amazonas, Brazil.

Data in brief
|November 29, 2023
PubMed
概括

MH-100K数据集提供了101,975个Android恶意软件样本,解决了基于机器学习的恶意软件检测高质量数据的稀缺问题. 该资源有助于评估和比较检测模型,并了解恶意软件的行为.

关键词:
安卓恶意软件是一种恶意软件.安卓安全安卓安全安卓安全机器学习是机器学习.恶意软件检测检测 恶意软件检测

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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:

  • 高质量的数据集对于有效的监督恶意软件检测模型至关重要.
  • 针对网络安全的机器学习的一个重大挑战是缺乏具有代表性和高质量的数据集.
  • 现有的数据集往往不足以提供全面的数据来进行强大的恶意软件分析.

研究的目的:

  • 介绍MH-100K数据集,这是一个大规模的Android恶意软件样本集合.
  • 为评估和比较基于机器学习的恶意软件分类器提供公共资源.
  • 为了促进研究安卓恶意软件的流行,行为和演变.

主要方法:

  • 将 101,975 个 Android 恶意软件样本编译成 MH-100K 数据集.
  • 在CSV文件中包含详细的元数据:SHA256哈希,包名,API调用,权限和意图.
  • 整合VirusTotal分析元数据以提供全面的样本信息.

主要成果:

  • MH-100K数据集包括101,975个安卓恶意软件样本与广泛的元数据.
  • 元数据包括166个权限,24,417个API调用和250个每个样本的意图.
  • 纳入了VirusTotal分析数据,使得对恶意软件特征有更深入的了解.

结论:

  • MH-100K数据集显著提高了Android恶意软件研究高质量数据的可用性.
  • 它支持对抗病毒扫描模式和恶意软件家族行为进行高级分析.
  • 预计这项资源将促进识别新的恶意软件变体和研究恶意软件演变的研究.