基于BERT组合的MBR框架用于安卓恶意软件检测
Faisal S Alsubaei1, Abdulwahab Ali Almazroi2, Walid Said Atwa2,3
1Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah, 21959, Saudi Arabia. fsalsubaei@uj.edu.sa.
Scientific reports
|April 24, 2025
概括
本研究介绍了一种新的BERT Ensemble (MBR) 框架,用于在物联网设备中检测Android恶意软件. MBR模型实现了高精度,为增强系统安全性和用户隐私提供了可靠的解决方案.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 安卓恶意软件对物联网设备构成重大风险.
- 现有的Android恶意软件检测方法在复杂的物联网环境中面临挑战.
研究的目的:
- 在基于推系统的物联网中开发一种用于Android恶意软件检测 (AMD) 的新框架.
- 为了增强系统安全和用户隐私,对抗不断发展的Android恶意软件威胁.
主要方法:
- 开发了一个与MobileNetV2结合的BERT Ensemble (MBR) 模型.
- 使用100个Android应用程序权限和精细的功能集的子集进行威胁分析技术.
- 集成方法应用于用于恶意软件检测的静态数据.
主要成果:
- MBR模型实现了98%的准确性,96%的精度,98%的回忆率和97%的F1得分.
- 与MCADS,DroidRL,CNN,FAGnet,GAN和FEDriod相比,该框架表现出更高的性能.
- 记录了0.058的低日志损失,表明模型可靠性很高.
结论:
- MBR框架为物联网中的Android恶意软件检测提供了可靠和创新的解决方案.
- 该研究解决了面对越来越多的Android恶意软件风险的关键用户隐私和系统安全问题.
- 这项研究提出了一种新的策略,用于恶意软件检测,使用对静态数据的集合方法.
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