面向园区网络的分层式流量区分系统的研究

发布时间：2018-03-29 10:08

本文选题：在线流量分类　切入点：分层式流量区分系统　出处：《济南大学》2014年硕士论文

【摘要】：随着互联网的普及，日益增长的网络流量和不断出现的各种网络新应用大大增加了网络的复杂性和管理难度，这严重威胁到网络服务质量和网络安全，对网络管理提出了巨大挑战。在这种情况下，网络流量分类具有良好的网络应用识别能力，并且能为网络管理提供信息支持，从而逐渐成为学术界研究的热点。网络流量因具有实时、易逝和不可再现的特性，致使网络流量分类必须拥有及时分类能力才更有实际意义，所以在线分类成为研究网络流量分类的重点。经过研究各种网络流量分类技术的原理和现状，我们认识到当前网络流量分类多以单点分类模式为主，其注重于分类准确率改善，却忽略了分类性能的不足而导致无法对日益增长的园区网络出口流量进行在线分类的问题。为了实现网络流量在线分类，本文打破单点流量分类的模式，将分布式思想引入到流量分类中，提出了一种分层式网络流量分类方法。这种方法是在园区网络的低速链路部署多个分类节点进行在线流量分类，并由数据中心对分类结果进行汇总来实现整个网络流量的在线分类。其主要特性：一是其避免了直接处理园区网络出口流量，，克服了单点性能不足而无法实现在线分类的缺陷；二是分类器动态加载机制能够实现对新应用识别和分类器的训练及动态更新，有效应对了概念漂移引起的分类准确率下降的现象。分层式网络流量分类不失为一种解决在线流量分类的有效方法。本文依据分层式网络流量分类方法设计了一个分层式网络流量分类模型，其包括中心节点、分类节点和Web集成管理等部分。中心节点和分类节点相辅相成的完成园区网络的流量区分任务。Web集成管理是对分类系统管理的Web实现。在分类模型的实现过程设计提出了分层式交互协议和分类器分发管理方法；实现了基于半监督学习的未知流量发现识别、分类器训练等功能；分别采用了LIBPCAP平台和可视化技术用于进行在线网络流量的捕获和结果显示等。最后经过实验和Web管理测试说明了分层式网络流量分类方法在线流量分类的可行性以及Web管理的实用性。
[Abstract]:With the popularity of the Internet, network traffic is increasing and the emergence of new network applications greatly increased network complexity and management difficulty, which is a serious threat to the network quality of service and network security, put forward a huge challenge to the network management. In this case, the network traffic classification has a good ability to identify network applications, and can provide information support for network management, which has gradually become the focus of academic research. The network traffic with real-time, perishable and irreproducible characteristics, resulting in network traffic classification and classification ability must have more practical significance, so online classification has become the focus of research on network traffic classification.
The principle and research status of all kinds of network traffic classification technology, we realize the network traffic classification by single point classification model, which focuses on improving the classification accuracy, but ignore the lack of classification performance and can lead to the online classification of campus network flow growing problem. In order to achieve network traffic online classification, this paper breaks the single point traffic classification model, will be distributed into traffic classification, a classification method of hierarchical network traffic. This is a method of online traffic classification in multi node classification low-speed link network deployment, and the data center on the classification results were aggregated to achieve the online classification of the whole network traffic. Its main characteristics: one is to avoid direct processing of campus network export flow, to overcome the shortcomings of single point performance can not be achieved The two is the defect of online classification; dynamic classifier training and dynamic loading mechanism can realize the new application in the identification and classification, to effectively deal with the concept of classification accuracy caused by the drift down phenomenon. Hierarchical network traffic classification is an effective method to solve the problem of online traffic classification.
Based on the hierarchical network traffic classification method to design a hierarchical network traffic classification model, which comprises a central node, node classification and Web integrated management. The central node and the node classification complement each other to complete the park network traffic differentiating task.Web integrated management is to realize the classification management system in the process of design and implementation of Web. The classification model proposed the method of hierarchical distribution management interaction protocol and classifier; to achieve a semi supervised learning based on identifying the unknown flow, classifier training and other functions; using LIBPCAP platform and visualization technology for online network traffic capture and display results. Finally, through experiment and Web test shows the feasibility of hierarchical management network traffic classification method for online traffic classification and Web management is practical.

【学位授予单位】：济南大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP393.06

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