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  本文選題：流量分類 + 混合模式�。� 參考：《蘇州大學》2014年碩士論文

【摘要】：網(wǎng)絡流量分類是指將混合有各種應用的流量按應用協(xié)議來進行分類，即鑒別網(wǎng)絡報文分組的應用類別的過程。網(wǎng)絡流量分類技術除了能夠為運營商提供更好的網(wǎng)絡服務以外，還能有效地進行監(jiān)督和管理網(wǎng)絡。因此，網(wǎng)絡流量分類在優(yōu)化網(wǎng)絡帶寬、提高網(wǎng)絡服務質量、對特定的應用進行計費、對惡意流量進行監(jiān)測以及確保網(wǎng)絡安全等方面有著極其重要的意義。 流量分類的過程主要包括兩個步驟：首先是選擇適當?shù)木W(wǎng)絡流屬性集，作為分類器所用數(shù)據(jù)集；其次，選擇適當?shù)膶W習方法對網(wǎng)絡流量進行分類。因而選擇普適性的特征集和合適的學習方法對于流量分類的結果至關重要。目前，現(xiàn)有的特征選擇技術使得特征集過度依賴于樣本空間，對于不同網(wǎng)絡環(huán)境的普適性較低，，而當前研究較多的學習方法是機器學習方法，但其計算效率較低，難以實現(xiàn)實時分類。 因此，本文針對上述問題，基于混合模式對網(wǎng)絡流量分類方法進行了優(yōu)化。研究工作主要包括三個方面：第一，本文從量綱分析法的角度對流統(tǒng)計特征進行了規(guī)約化，并推導出一組普適性流量特征集；第二，由于深度數(shù)據(jù)包檢測技術相對于統(tǒng)計學習方法來說更為準確、高效、移植性好，是目前商用流量分類系統(tǒng)的主要技術選擇，但無法適用于加密流量，而機器學習方法能解決這個問題。因此，本文采用的學習方法是機器學習和深度數(shù)據(jù)包檢測技術的混合技術（即混合模式）；第三，構建了一個分布式平臺。通過該平臺利用混合方法對流量進行檢測和分類處理，利用多個集群系統(tǒng)進行并行處理、監(jiān)督和調度，以達到平攤資源、避免系統(tǒng)資源崩潰的效果，并且對負載均衡算法進行了改進，從而實現(xiàn)了資源的綜合利用，提高了優(yōu)化效率。 實驗結果表明，在高帶寬骨干網(wǎng)現(xiàn)網(wǎng)復雜流量類別中，本文推導出的規(guī)約化方法具有一定的普適性，用于分類的時間較短，效率更高。同時本文在該局域網(wǎng)環(huán)境下，利用規(guī)約化的數(shù)據(jù)集進行基于混合模式的分類實驗，并分別與以往的這種混合技術以及單一的深度數(shù)據(jù)包檢測技術進行對比，結果表明基于混合模式的網(wǎng)絡流量分類平臺規(guī)模較小，分類速度相對更快，從而進一步說明了系統(tǒng)優(yōu)化的效果。
[Abstract]:Network traffic classification refers to the process of classifying traffic mixed with various applications according to application protocols, that is, to identify the application categories of network packet packets. Network traffic classification technology not only can provide better network services for operators, but also can effectively supervise and manage the network. Therefore, network traffic classification is of great significance in optimizing network bandwidth, improving network quality of service, charging specific applications, monitoring malicious traffic and ensuring network security. The process of traffic classification mainly includes two steps: first, selecting the appropriate attribute set of network flow as the data set used by classifier; secondly, selecting the appropriate learning method to classify network traffic. Therefore, the selection of universal feature sets and appropriate learning methods is very important to the result of traffic classification. At present, the existing feature selection technology makes the feature set excessively dependent on the sample space, and the universality of different network environments is low. However, machine learning is the most widely studied learning method, but its computational efficiency is low. It is difficult to realize real-time classification. Therefore, in order to solve the above problems, this paper optimizes the network traffic classification method based on hybrid mode. The research work mainly includes three aspects: first, this paper normalizes the characteristics of convection statistics from the perspective of dimensional analysis, and deduces a set of universal flow characteristic sets; second, Since the depth packet detection technology is more accurate, efficient and portable than the statistical learning method, it is the main technical choice of the current commercial traffic classification system, but it can not be applied to encrypted traffic. Machine learning can solve this problem. Therefore, the learning method adopted in this paper is a hybrid technology of machine learning and depth packet detection (i.e. hybrid mode). Thirdly, a distributed platform is constructed. The platform uses hybrid method to detect and classify the traffic, and uses multiple cluster systems to process, supervise and schedule in parallel, so as to achieve the effect of sharing resources and avoiding the collapse of system resources. The load balancing algorithm is improved to realize the comprehensive utilization of resources and improve the efficiency of optimization. The experimental results show that in the complex traffic classes of high-bandwidth backbone networks, the proposed regularization method has a certain universality, shorter time for classification and higher efficiency. At the same time, in this LAN environment, the canonical data set is used to carry out the classification experiment based on hybrid mode, and compared with the previous hybrid technology and the single depth data packet detection technology. The results show that the network traffic classification platform based on hybrid mode is smaller in scale and faster in classification speed, which further explains the effect of system optimization.
【學位授予單位】：蘇州大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP393.06

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本文編號：2055791