本文選題：網(wǎng)絡流量分類 + 最小最大規(guī)則　； 參考：《南京郵電大學》2017年碩士論文

【摘要】：對網(wǎng)絡流量進行分析與分類是實現(xiàn)網(wǎng)絡監(jiān)控和管理的一大途徑,并被廣泛應用于網(wǎng)絡入侵檢測系統(tǒng)、網(wǎng)絡管理系統(tǒng)等領域中。然而,隨著動態(tài)端口號技術以及對流量加密技術的發(fā)展,單純傳統(tǒng)的網(wǎng)絡流量分類方法已經(jīng)無法達到我們對其準確性的要求。近年來,基于機器學習的網(wǎng)絡流量分類受到廣泛關注,其僅需定義一組與流量相關的統(tǒng)計量作為特征,而不需要使用端口號等來表示流量,從而避免了傳統(tǒng)方法帶來的局限性。然而,網(wǎng)絡流量分類中存在著諸如類別不平衡、數(shù)據(jù)規(guī)模大等各種問題,若僅單純使用傳統(tǒng)的機器學習算法同樣會導致分類性能較差。本文以此為出發(fā)點,對機器學習算法進行研究和改進,并用于網(wǎng)絡流量分類中以提高其性能。本文提出一種基于最小最大策略的集成特征選擇算法用于解決流量分類中遇到的類別不平衡問題。該算法是將機器學習中特征選擇和集成學習相結合,主要分為兩個步驟,即數(shù)據(jù)劃分與特征選擇結果集成。先通過某方法將原始數(shù)據(jù)集劃分為若干數(shù)據(jù)子集,在對每個數(shù)據(jù)子集進行特征選擇過后,再通過最小最大策略將每個數(shù)據(jù)子集的特征選擇結果進行集成,得到最終的特征選擇結果。本文通過將該算法與其他集成特征選擇算法進行比較,主要驗證其在網(wǎng)絡流量分類中的性能。為了進一步提升網(wǎng)絡流量分類的性能,本文通過考慮流量之間的相關性,在之前的流量數(shù)據(jù)集的基礎上提取了一組基于多條流量在時間/空間上的關聯(lián)性得到的特征,如與待分類流量擁有相同源IP地址的流量集合中流量的數(shù)量等。最后將提取了多流特征后的數(shù)據(jù)集使用提出的集成特征選擇策略進行特征選擇并進行分類以驗證多流特征對網(wǎng)絡流量分類效果的影響。實驗表明,其在結合了部分多流特征之后,效果明顯地提升。本文提出的集成特征選擇算法能有效地處理流量分類中類別不平衡的問題。與此同時,提取的多流特征也對流量分類的性能有一定地提升。
[Abstract]:The analysis and classification of network traffic is a great way to realize network monitoring and management, and is widely used in network intrusion detection system, network management system and other fields. However, with the development of dynamic port number technology and traffic encryption technology, the traditional network traffic classification method can not meet the requirements of its accuracy. In recent years, network traffic classification based on machine learning has attracted much attention. It only needs to define a set of statistics related to traffic as a feature, and does not need to use port numbers to represent traffic, thus avoiding the limitations of traditional methods. However, there are many problems in network traffic classification, such as class imbalance, large data scale and so on. If we only use traditional machine learning algorithms, the classification performance will also be poor. This paper studies and improves the machine learning algorithm and applies it to network traffic classification to improve its performance. In this paper, an ensemble feature selection algorithm based on minimum maximum strategy is proposed to solve the class imbalance problem in traffic classification. The algorithm is a combination of feature selection and ensemble learning in machine learning, which is divided into two steps: data partitioning and feature selection result integration. First, the original data set is divided into several data subsets by a certain method. After the feature selection of each data subset is carried out, the feature selection results of each data subset are integrated by the minimum maximum strategy. The final feature selection results are obtained. By comparing the algorithm with other integrated feature selection algorithms, this paper mainly verifies its performance in network traffic classification. In order to further improve the performance of network traffic classification, by considering the correlation between traffic, we extract a set of features based on the correlation of multiple traffic in time / space based on the previous traffic data set. Such as the amount of traffic in the traffic set with the same source IP address as the traffic to be classified. Finally, the data set after extracting multi-stream features is selected and classified using the proposed integrated feature selection strategy to verify the effect of multi-flow features on network traffic classification. The experimental results show that the effect is improved obviously by combining partial multi-flow features. The integrated feature selection algorithm proposed in this paper can effectively deal with the problem of class imbalance in traffic classification. At the same time, the extracted multi-stream features also improve the performance of traffic classification.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP393.06;TP181

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