半2DCAE:一个半监督的2D-CNN自动编码器模型,用于特征表示和加密流量分类
Jun Cui1, Longkun Bai2, Guangxu Li2
1Tiangong University, School of Life Sciences, Tianjin, China.
PeerJ. Computer science
|December 11, 2023
概括
本研究介绍了一种新的半监督二维卷积自编码器 (Semi-2DCAE) 模型,用于增强网络流量分类. 该FlowSpectrum模型在对加密流量进行分类时实现了高精度,超过了现有的解决方案.
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:
- 随着加密互联网流量的增加,传统的流量分类方法 (基于端口,基于DPI) 的效率下降.
- 现有的机器学习和深度学习方法在提取代表性流量结构特征方面扎.
研究的目的:
- 提出一种新型模型,半监督的二维卷积自动编码器 (Semi-2DCAE),用于准确的网络流量分类.
- 为了解决加密流量的特征提取和表示方面的局限性.
主要方法:
- 使用二维卷积神经网络 (2D-CNN) 来从网络流量中提取空间结构特征.
- 使用自动编码器结构来缩小数据,创建一个"FlowSpectrum"表示.
- 集成了PRuLe激活功能,以确保培训的稳定性.
主要成果:
- 流谱模型有效地描述了在一个一维坐标系中加密的流量特征.
- 实现了非VPN加密流量分类的99.2%准确度,超过了最先进的状态约7%.
- 在VPN加密流量分类方面获得了98.3%的准确性,比最先进的技术性能高出约2%.
结论:
- 拟议的带有FlowSpectrum表示的Semi-2DCAE模型显著提高了加密流量分类的准确性.
- 这种方法在广泛加密的时代为网络管理,监控和安全提供了强大的解决方案.
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