本文選題：P2P + 流量識別��； 參考：《湖北工業(yè)大學》2014年碩士論文

【摘要】：由于互聯(lián)網(wǎng)本身是基于點到點的傳輸，使得P2P技術(shù)得到了廣泛地應(yīng)用與發(fā)展，給用戶帶來便捷的同時也給網(wǎng)絡(luò)質(zhì)量和網(wǎng)絡(luò)管理帶來了巨大的負面影響，如網(wǎng)絡(luò)擁堵、知識產(chǎn)權(quán)、資源管理以及安全隱患問題。P2P流量識別問題得到了越來越多的關(guān)注，，圍繞P2P識別問題產(chǎn)生了一批相關(guān)的算法。近年來研究與應(yīng)用最為廣泛的P2P流量識別方法之一是基于機器學習的識別方法。然而由于P2P網(wǎng)絡(luò)的突變性和不確定性，對于傳統(tǒng)的基于貝葉斯網(wǎng)絡(luò)，決策樹算法等機器學習方法而言，P2P流量識別變得更加困難。 支持向量機（Support Vector Machine，簡稱SVM）作為目前性能良好且廣泛使用的分類器，它在克服“維數(shù)災(zāi)難”以及避免局部最優(yōu)解等流量識別問題上具有明顯的優(yōu)勢。然而在P2P流量識別問題中，支持向量機的性能受懲罰系數(shù)和核函數(shù)的參數(shù)的影響。常規(guī)的支持向量機參數(shù)求取方法性能需要進一步加強。菌群優(yōu)化算法是近年來新提出的一種群體智能優(yōu)化算法，具有較強的尋優(yōu)能力。小波函數(shù)可以用來描述突變信號逐漸精細的特點，能夠在一定程度上處理突變的P2P網(wǎng)絡(luò)。因此本文重點研究菌群優(yōu)化算法和小波支持向量機在P2P流量識別問題中的應(yīng)用，本文的主要研究內(nèi)容和工作如下： 1提出了一種結(jié)合菌群優(yōu)化算法和支持向量機的P2P流量識別方法。首先引入菌群優(yōu)化算法來優(yōu)化支持向量機的兩個參數(shù)，從而可以得到參數(shù)配置較優(yōu)的支持向量機；并將其應(yīng)用于P2P流量識別。通過與現(xiàn)有的基于遺傳算法優(yōu)化參數(shù)和基于粒子群算法優(yōu)化參數(shù)的支持向量機方法在實際的P2P流量識別問題中進行性能對比測試，結(jié)果顯示基于菌群優(yōu)化算法所優(yōu)化的支持向量機在性能上更具優(yōu)勢。 2在優(yōu)化了支持向量機的參數(shù)以后，對配置不同核函數(shù)的支持向量機進行P2P流量識別性能測試和分析。由于小波分析在局部分析和處理突變信號方面的優(yōu)越性，能很好地解決P2P網(wǎng)絡(luò)流量的突變性和不確定性，這里重點研究選擇合適的小波核函數(shù)來提高支持向量機的性能，通過對常用的核函數(shù)及多種小波核函數(shù)的對比實驗，結(jié)果表明基于小波核函數(shù)和基于菌群優(yōu)化算法的SVM在P2P流量識別具有較高的識別精度與穩(wěn)定性。
[Abstract]:Since the Internet itself is based on point-to-point transmission, P2P technology has been widely used and developed, which brings convenience to users, but also brings huge negative effects to network quality and network management, such as network congestion, intellectual property rights.More and more attention has been paid to resource management and P2P traffic identification.In recent years, one of the most widely studied and applied P2P traffic identification methods is based on machine learning.However, due to the mutation and uncertainty of P2P network, it is more difficult for traditional machine learning methods based on Bayesian network and decision tree algorithm to identify P2P traffic.As a widely used classifier with good performance at present, support Vector Machine (SVM) has obvious advantages in overcoming "dimension disaster" and avoiding the problem of local optimal solution.However, in P2P traffic identification problem, the performance of SVM is affected by the penalty coefficient and the parameters of kernel function.The performance of the conventional support vector machine (SVM) parameter estimation method needs to be further enhanced.Bacterial colony optimization algorithm is a new swarm intelligence optimization algorithm proposed in recent years.Wavelet function can be used to describe the characteristics of the gradual refinement of abrupt signals and to deal with sudden changes in P2P networks to a certain extent.Therefore, this paper focuses on the application of microbial colony optimization algorithm and wavelet support vector machine in P2P traffic identification. The main contents and work of this paper are as follows:1. A P2P traffic identification method combined with microbial colony optimization algorithm and support vector machine is proposed.Firstly, the bacterial colony optimization algorithm is introduced to optimize the two parameters of support vector machine (SVM), so that the support vector machine with better parameter configuration can be obtained, and it is applied to P2P traffic identification.Compared with the existing support vector machine (SVM) based on genetic algorithm and particle swarm optimization parameters, the performance of P2P traffic identification problem is compared and tested.The results show that the support vector machine based on colony optimization algorithm has more advantages in performance.2 after optimizing the parameters of support vector machine, the P2P traffic identification performance of support vector machine with different kernel function is tested and analyzed.Due to the advantages of wavelet analysis in local analysis and processing of abrupt signals, the mutation and uncertainty of P2P network traffic can be solved well. This paper focuses on the selection of appropriate wavelet kernel functions to improve the performance of support vector machines.The comparative experiments of common nuclear function and several wavelet kernel functions show that SVM based on wavelet kernel function and bacterial colony optimization algorithm has high recognition accuracy and stability in P2P traffic identification.
【學位授予單位】：湖北工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP181;TP393.06

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