MVDroid:一个使用神经网络的安卓恶意VPN检测器
Saeed Seraj1, Siavash Khodambashi2, Michalis Pavlidis1
1School of Architecture, Technology and Engineering, University of Brighton, Brighton, BN2 4GJ UK.
Neural computing & applications
|June 26, 2023
概括
许多虚拟私人网络 (VPN) 损害了用户的隐私和安全. 这项研究引入了一个深度学习模型,以准确识别恶意的Android VPN,保护用户免受数据盗窃和恶意软件的侵害.
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:
- 虚拟私人网络 (VPN) 广泛用于在线隐私,但许多网络存在重大安全风险.
- 恶意VPN可以窃取数据,传播恶意软件,并破坏用户隐私,尽管它们看起来合法.
- 由于不值得信赖的VPN应用程序的普及,Android用户面临着特殊的风险.
研究的目的:
- 开发一个优化的深度学习模型来检测恶意的Android VPN.
- 创建一个新的数据集,包括恶意和良性Android VPN应用程序,用于培训和评估.
- 通过识别和标记不安全的VPN来增强Android用户的安全性.
主要方法:
- 一个优化的深度学习神经网络被设计和实施.
- 该模型使用新编辑的Android VPN数据集进行了训练和评估.
- 应用程序权限被用作识别恶意VPN行为的关键功能.
主要成果:
- 拟议的深度学习分类器在识别恶意VPN方面取得了很高的准确性.
- 与标准分类器相比,该模型在准确性,精度和回忆等关键指标上表现出卓越的性能.
- 实验结果验证了深度学习方法在检测不安全VPN方面的有效性.
结论:
- 开发的深度学习模型为识别恶意Android VPN提供了可靠的解决方案.
- 这项研究有助于改善通过VPN寻求隐私的移动用户的安全环境.
- 这些发现强调了强有力的安全措施在VPN应用程序的重要性.
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