【摘要】：隨著網(wǎng)絡(luò)用戶數(shù)量和網(wǎng)絡(luò)應(yīng)用規(guī)模的快速增長,基于TCP/IP協(xié)議的互聯(lián)網(wǎng)的應(yīng)用種類越來越多。面對(duì)各種具有充分反監(jiān)測能力的互聯(lián)網(wǎng)應(yīng)用的出現(xiàn),傳統(tǒng)的基于端口和應(yīng)用層載荷特征的識(shí)別方法已經(jīng)難以勝任當(dāng)前或者將來流量識(shí)別的需求。高效、準(zhǔn)確、智能、實(shí)時(shí)地進(jìn)行互聯(lián)網(wǎng)流量識(shí)別成了一個(gè)具有高度挑戰(zhàn)性的問題。本文研究了基于決策樹的網(wǎng)絡(luò)流量分類系統(tǒng)的設(shè)計(jì)與實(shí)現(xiàn),主要工作如下： 首先介紹了基于端口和基于數(shù)據(jù)深度包的網(wǎng)絡(luò)流量分類模型的特點(diǎn)及問題,分析了基于機(jī)器學(xué)習(xí)的網(wǎng)絡(luò)流量分類方法,比較了貝葉斯分類模型、決策樹分類模型及其基本思想,并介紹了特性選擇采用的常見算法。 其次給出了流量分類系統(tǒng)的流程設(shè)計(jì)與功能結(jié)構(gòu),系統(tǒng)包括四個(gè)模塊,流量采集模塊,流量特性統(tǒng)計(jì)模塊,流量分類模塊和分類結(jié)果顯示模塊。之后分析了決策樹的構(gòu)造思路,設(shè)計(jì)了流量分類系統(tǒng)中的C5.0決策樹分類器。 最后給出了流量采集的實(shí)現(xiàn)方法、特性統(tǒng)計(jì)的實(shí)現(xiàn)方法以及實(shí)際網(wǎng)絡(luò)流量分類結(jié)果的顯示方法,并展示分析了系統(tǒng)的用戶界面及對(duì)實(shí)際網(wǎng)絡(luò)流量進(jìn)行分類的結(jié)果,驗(yàn)證了系統(tǒng)的有效性。 論文結(jié)合機(jī)器學(xué)習(xí)在流量識(shí)別研究中的應(yīng)用,把C5.0決策樹應(yīng)用到網(wǎng)絡(luò)流量的識(shí)別分類中,設(shè)計(jì)并實(shí)現(xiàn)了一個(gè)基于決策樹的流量分類系統(tǒng),可實(shí)現(xiàn)對(duì)實(shí)際網(wǎng)絡(luò)流量進(jìn)行分類。
[Abstract]:With the rapid growth of the number of network users and the scale of network applications, there are more and more kinds of Internet applications based on TCP/IP protocol. In the face of the emergence of various Internet applications with sufficient anti-monitoring ability, the traditional identification methods based on port and application layer load characteristics have been unable to meet the needs of current or future traffic identification. Efficient, accurate, intelligent and real-time Internet traffic identification has become a highly challenging problem. In this paper, the design and implementation of network traffic classification system based on decision tree are studied. The main work is as follows: firstly, the characteristics and problems of network traffic classification model based on port and data depth packet are introduced. This paper analyzes the network traffic classification method based on machine learning, compares the Bayesian classification model, decision tree classification model and their basic ideas, and introduces the common algorithms used in characteristic selection. Secondly, the flow design and functional structure of the traffic classification system are given. The system includes four modules: traffic collection module, traffic characteristic statistics module, traffic classification module and classification result display module. Then the construction idea of decision tree is analyzed, and the c5.0 decision tree classifier in traffic classification system is designed. Finally, the realization method of traffic collection, the realization method of characteristic statistics and the display method of the actual network traffic classification results are given, and the user interface of the system and the results of classifying the actual network traffic are displayed and analyzed. The effectiveness of the system is verified. Combined with the application of machine learning in traffic identification research, this paper applies c5.0 decision tree to the identification and classification of network traffic, and designs and implements a traffic classification system based on decision tree, which can classify the actual network traffic.
【學(xué)位授予單位】：中國科學(xué)院大學(xué)（工程管理與信息技術(shù)學(xué)院）
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP393.06

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本文編號(hào)：2492445