用属性和基于图形的表征进行零拍摄的流量识别,用于边缘计算
Zikui Lu1, Zixi Chang2, Mingshu He3
1School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
本研究介绍了AG-ZSL,这是一种用于加密流量分类的新型零射击学习框架. 它通过从流量行为和属性中学习,有效地识别未知的流量类型,改善网络安全.
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:
- 细粒度的流量识别至关重要,但由于移动设备的扩散和网络的增长,这具有挑战性.
- 现有的机器学习和深度学习方法因依赖训练数据分布而难以处理未见的流量数据.
研究的目的:
- 提出AG-ZSL,一个用于一般加密流量分类的零射击学习框架.
- 为了解决当前处理隐形交通样本的方法的局限性.
主要方法:
- AG-ZSL学习了两个映射函数:来自交互图的交通行为嵌入和来自描述的属性嵌入.
- 它将这些嵌入在共享功能空间中的距离最小化.
- 使用梯度拒绝和K-最接近邻居的两步分类方法.
主要成果:
- 在分类已知和未知的交通类型方面,AG-ZSL表现出了卓越的表现.
- 该框架显示了物联网 (IoT) 数据集的高效性.
结论:
- AG-ZSL为一般加密流量分类提供了一个强大的解决方案,特别是对于看不见的样本.
- 该框架具有显著的潜力,可以提高网络边缘的安全和高效的网络流量管理.
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