ViTDroid:视觉转换器,以高效,可解释地关注Android二进制文件中的恶意行为
Toqeer Ali Syed1, Mohammad Nauman2, Sohail Khan2
1Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
新的深度学习模型ViTDroid通过识别恶意代码指令来分析Android恶意软件. 这种可解释的AI方法有助于恶意软件检测和分析的专家,提高了对不断增加的移动威胁的安全性.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 安卓主导移动操作系统市场 (71%的份额),使其用户容易受到越来越多的移动恶意软件威胁.
- 传统的恶意软件分析很难跟上移动恶意软件不断增长的数量和复杂性.
- 深度学习模型虽然在图像分析中有效,但在解释恶意软件特征方面面临挑战,并且存在诸如CNN中的翻译不变性等局限性.
研究的目的:
- 介绍ViTDroid,一个新的深度学习模型,利用视觉转换器来分析Android恶意软件的opcode序列.
- 提高恶意软件分析中的深度学习模型的可解释性,超越简单的分类.
- 为Android恶意软件样本中导致恶意行为的特定指令提供可操作的见解.
主要方法:
- 开发了ViTDroid,这是一个基于视觉转换器的深度学习模型,用于分析Android恶意软件的opcode序列.
- 在大型的,真实世界的Android恶意软件样本数据集上训练和评估模型.
- 专注于通过识别恶意行为引起的指令来实现可解释的预测.
主要成果:
- 实现了0.0019的低虚假阳性率,超过了之前的0.0021.
- 证明了该模型不仅能够准确地对恶意软件进行分类,而且还能够精确地确定特定的恶意指令.
- 为恶意软件分类背后的原因提供了可解释的见解.
结论:
- 通过可解释的预测,ViTDroid在基于深度学习的Android恶意软件分析方面取得了重大进展.
- 该模型识别恶意指令的能力有助于人类专家,提高了整体恶意软件分析过程.
- ViTDroid通过提供对恶意软件行为更深入的见解,并促进更有效的威胁检测,为提高网络安全做出了贡献.
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