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首页> 外文会议>2017 International Conference on Inventive Computing and Informatics >Multi level statistical classification of network traffic
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Multi level statistical classification of network traffic

机译：网络流量的多级统计分类

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摘要

Network Traffic Classification is the crucial phase of network monitoring. The network traffic once categorized per application it can be imposed with appropriate security policies to improve the performance of the network. The port and payload based traffic classification techniques used in the past decade relapsed owing to new techniques of encryption and tunneling emerging day-by-day. Recently, the statistical classification employing data mining techniques and analyzing the attributes for characterization of network traffic proved high efficiency. This proposed work characterized the network traffic based on semi-supervised machine learning approach. It initially clustered the auxiliary and content flow of application to precisely track the footprint of the application using improved k-means clustering which in turn is fed into C5.0 classifier to construct the classifier model. This system is designed to classify the network traffic accurately that resulted in high F-Score value of 0.993.
机译：网络流量分类是网络监控的关键阶段。一旦按应用对网络流量进行了分类，就可以对其施加适当的安全策略以改善网络性能。在过去的十年中，基于端口和有效负载的流量分类技术由于新的加密和隧道技术日新月异而重新出现。最近，事实证明，采用数据挖掘技术并分析属性以表征网络流量的统计分类具有很高的效率。这项工作基于半监督机器学习方法来表征网络流量。最初，它使用改进的k均值聚类对应用程序的辅助流和内容流进行聚类，以精确跟踪应用程序的覆盖区，然后将其输入C5.0分类器中以构建分类器模型。此系统旨在对网络流量进行准确分类，从而导致F-Score值高达0.993。

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