GEAAD:对安卓恶意软件防御产生逃避性的对抗性攻击
Naveed Ahmad1, Amjad Saleem Rana2, Hassan Jalil Hadi3,4
1Prince Sultan University, Riyadh, Saudi Arabia. nahmed@psu.edu.sa.
Scientific reports
|April 8, 2025
概括
安卓恶意软件检测面临着来自对立例子的挑战. 一个新的双对手生成对抗网络 (DOpGAN) 通过错误分类恶意代码来逃避检测,突出了对先进安全防御的需求.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 移动安全 移动安全
背景情况:
- 安卓的主导地位使其成为网络攻击的目标.
- 恶意软件检测系统对于对抗不断变化的威胁至关重要.
- 敌对的例子越来越多地被用来逃避检测.
研究的目的:
- 调查生成对抗网络 (GAN) 在Android恶意软件检测中的有效性.
- 分析对抗示例的影响,特别是来自双对手生成对抗网络 (DOpGAN),对恶意软件检测系统的影响.
- 提出加强安卓安全的策略,以防止复杂的规避技术.
主要方法:
- 利用机器学习的静态和动态分析,专注于GANs.
- 实现了安卓Opcode修改GAN与Opcode频率最佳调整算法.
- 介绍并分析了双对手生成对抗网络 (DOpGAN) 用于生成对抗示例.
主要成果:
- 安卓Opcode修改GAN通过修改opcode分布来改善恶意软件检测.
- DOpGAN展示了一个灰色盒子攻击策略,成功地将恶意软件错误分类为良性并逃避检测.
- 由DOpGAN生成的对抗性示例凸显了当前检测系统面临的重大挑战.
结论:
- 由DOpGAN生成的对抗示例对Android安全构成重大威胁.
- 有必要将对抗性示例检测系统集成到Android安全框架中.
- 持续的创新和协作对于抗击新出现的威胁的性Android安全至关重要.
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