安卓德克斯:安卓德克斯 隐藏的恶意软件图像
Sana Aurangzeb1, Muhammad Aleem1, Muhammad Taimoor Khan2
1National University of Computing and Emerging Sciences (FAST-NUCES), Department of Computer Science, Islamabad, 44000, Pakistan.
Scientific data
|February 16, 2024
概括
研究人员开发了AndroDex,这是一个大型的Android恶意软件数据集,包含来自180多个家庭的24,746个.dex图像. 这一数据集有助于Android恶意软件的检测和分类,即使使用模糊化技术.
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:
- 越来越多的网络威胁需要强大的Android恶意软件检测.
- 现有的恶意软件数据集可能无法充分解决模糊化技术.
- 大规模,可靠的数据集对于开发有效的恶意软件分类器至关重要.
研究的目的:
- 为了介绍AndroDex,一个新的Android恶意软件数据集.
- 为增强Android恶意软件检测和分类研究提供资源.
- 为了促进基于深度学习的恶意软件分析.
主要方法:
- 在180多个家族中收集了24,746个Android恶意软件样本.
- 将恶意软件的便携式可执行 (.dex) 文件转换为图像格式.
- 在图像转换之前,对恶意软件样本应用了模糊化技术.
主要成果:
- 安卓Dex数据集包括24,746个恶意软件样本,以图像形式表示.
- 基于图像的表示有效地可视化恶意软件的特征,绕过模糊化.
- 该数据集支持.apk文件的静态分析,提供一个时间和空间高效的初步检查.
结论:
- 安卓Dex是一个有价值的资源,用于推进Android恶意软件的检测和分类.
- 恶意软件图像分析提供了一种有希望的方法来克服模糊化挑战.
- 该数据集促进了apk文件的初始静态分析,有助于恶意软件研究.
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