基于网络的生成对抗数据增强用于增强无线物理层身份验证
Lamia Alhoraibi1, Daniyal Alghazzawi1, Reemah Alhebshi1
1Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Sensors (Basel, Switzerland)
|January 26, 2024
概括
这项研究通过使用深度学习来对节点分类来增强无线安全性. 生成对抗网络和卷积神经网络将分类精度提高了19%,解决了数据集的局限性.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 无线通信无线通信
背景情况:
- 无线物理层认证对于强大的无线安全至关重要.
- 深度学习技术在无线节点的分类和识别方面显示出显著的前景.
- 一个主要的挑战是缺乏足够的数据集来训练这个领域的深度学习模型.
研究的目的:
- 通过深度学习开发和评估用于无线节点分类的数据驱动方法.
- 通过自动化数据增强来解决数据集稀缺问题.
- 为了提高无线物理层认证模型的准确性.
主要方法:
- 利用生成对抗网络 (GAN) 进行自动化数据增强.
- 应用卷积神经网络 (CNN) 进行无线节点分类.
- 使用原始数据集和合成生成的数据集比较模型性能.
主要成果:
- 提出的数据驱动模型证明了有效的无线节点分类.
- 使用GAN进行数据增强有助于改进模型训练.
- 与基线相比,分类准确率大约增加了19%.
结论:
- 深度学习,特别是GAN和CNN,为无线节点分类和认证提供了强大的解决方案.
- 自动数据增强是克服无线安全研究数据集局限性的可行策略.
- 开发的模型显示了增强无线安全系统的巨大潜力.
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