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首页> 外文会议>IEEE Conference on Local Computer Networks >Unsupervised Traffic Flow Classification Using a Neural Autoencoder
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Unsupervised Traffic Flow Classification Using a Neural Autoencoder

机译：使用神经自动编码器的无监督交通流分类

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摘要

To cope with the varying delay and bandwidth requirements of today's mobile applications, mobile wireless networks can profit from classifying and predicting mobile application traffic. State-of-the-art traffic classification approaches have various disadvantages: port-based classification methods can be circumvented by choosing non-standard ports, protocol fingerprinting can be confused by the use of encryption, and current supervised learning methods for analyzing the statistical properties of network flows try to detect predefined classes, such as e-mail or FTP traffic, learned during training. In this paper, we present a novel approach to unsupervised traffic flow classification using statistical properties of flows and clustering based on a neural auto encoder. A novel time interval based feature vector construction and a semi-automatic cluster labeling method facilitate traffic flow classification independent of known traffic classes. An experimental evaluation on real data captured over a period of four months is presented. The obtained results show that 7 different classes of mobile traffic flows are detected with an average precision of 80% and an average recall of 75%.
机译：为了应对当今移动应用程序不断变化的延迟和带宽要求，移动无线网络可以从分类和预测移动应用程序流量中受益。最新的流量分类方法有很多缺点：可以通过选择非标准端口来规避基于端口的分类方法，可以通过使用加密来混淆协议指纹识别，以及用于分析统计属性的当前监督学习方法的网络流尝试检测在培训过程中学习到的预定义类别，例如电子邮件或FTP流量。在本文中，我们提出了一种新的方法，该方法利用流量的统计属性和基于神经自动编码器的聚类进行无监督交通流分类。一种新颖的基于时间间隔的特征向量构造和一种半自动的聚类标记方法，有助于与已知交通类别无关的交通流分类。提出了对四个月内捕获的真实数据的实验评估。获得的结果表明，检测到7种不同类别的移动业务流，其平均精度为80％，平均召回率为75％。

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