基于机器学习的即时通信流量分类技术

发布时间：2024-04-02 02:21

　　论文对主要的网络流量分类技术进行了阐述,并提出了目前网络流量分类技术所面临的问题。然后论文主要研究了以下内容:准确的即时通信(IM)流量分类方法,有效的特征选择方法和用于IM流量分类的有效特征包数的界定方法。为了提高IM流量分类精度,稳定性和分类性能,论文提出了一些算法模型,具体贡献如下:1、论文在研究和分析即时通信流量的特点和相关机器学习的理论和方法的基础上,首先选择支持向量机(SVM)、C4.5决策树、贝叶斯网络和朴素贝叶斯这4种经典机器学习分类器进行即时通信文本流量分类的研究,并在两种不同网络环境下采集即时通信流量数据做为数据集。然后,提取了50个流量特征用于训练和测试。实验结果表明,所有分类器在准确率、召回率和精度指标方面都非常有效,但其中C4.5机器学习分类器的性能最佳。2、基于机器学习的流量分类中,不恰当的特征选取容易产生错误的流量分类结果,因此即时通信流量分类特征的选取也是即时通信流量分类中的一个挑战性问题。为了解决这个问题,论文提出了一种特征选择度量标准Weighted Mutual Information(WMI),在此基础上提出了一个WMI_AC...

【文章页数】：153 页

【学位级别】：博士

【文章目录】：
Abstract
摘要
Chapter1 Introduction
    1.1 Research Background
        1.1.1 Port based traffic classification
        1.1.2 Deep packet inspection
        1.1.3 Machine learning based traffic identification
    1.2 Survey of related work
        1.2.1 Network traffic classification feature description and extraction
        1.2.2 Supervised learning traffic classification
        1.2.3 Un-Supervised learning traffic classification
        1.2.4 Early stage traffic classification
    1.3 Practical Background of Network Traffic Classification
        1.3.1 Bayes net machine learning classifier
        1.3.2 Na?ve Bayes machine learning classifier
        1.3.3 Support vector machine learning classifier
        1.3.4 C4.5 decision tree machine learning classifier
    1.4 Internet trace traffic data set
    1.5 IM traffic classification result analysis
        1.5.1 Performance measurement
        1.5.2 Results and analysis
        1.5.3 Contributions for IM traffic classification research
    1.6 Structure of Thesis
Chapter2 Effective Feature Selection for IM Application Traffic Classification
    2.1 Introduction
    2.2 Feature Selection Metrics
        2.2.1 Mutual Information Based Metric
    2.3 Proposed Method
        2.3.1 Weighted Mutual Information(WMI)Metric
        2.3.2 ACC Metric
        2.3.3 WMI_ACC Algorithm
        2.3.4 Statistical Test
    2.4 Evaluation Methodology
        2.4.1 Data Sets
        2.4.2 HIT Trace I Dataset
        2.4.3 NIMS Dataset
        2.4.4 Performance Measures
    2.5 Experimental Results and Analysis
        2.5.1 Wilcoxon Pairwise Statistical Test Result
        2.5.2 Selected Features of Our Propose Algorithm
        2.5.3 Comparison
    2.6 Analysis and Discussion
    2.7 Summary
Chapter3 Feature Selection for Imbalance IM Applications Traffic Classification
    3.1 Introduction
    3.2 Related Work
    3.3 Methodology
        3.3.1 Feature Selection Metrics
        3.3.2 AUC Metric
        3.3.3 Feature Selection Algorithms
        3.3.4 WMI_AUC Algorithm
        3.3.5 RFS Algorithm
    3.4 Evaluation Methodology
        3.4.1 Data Sets
        3.4.2 Evaluation Criteria for Performance Measurements
    3.5 Experimental Results and Analysis
        3.5.1 Analysis Results of HIT Trace1 Dataset
        3.5.2 Analysis Results of NIMS Dataset
    3.6 Analysis and Comparison
    3.7 Summary
Chapter4 Robust Feature Selection Approach for IM Applications Traffic
    4.1 Introduction
    4.2 Methodology
        4.2.1 FSA and FEA Proposed Methods
        4.2.2 Feature Selection Based Metrics
        4.2.3 Symmetrical Uncertainty Based Metric:
    4.3 The Feature Selection Approach(FSA):
        4.3.1 The Proposed Algorithm
        4.3.2 The Feature Evaluation Approach(FEA):
    4.4 Evaluation Methodology
        4.4.1 Datasets
        4.4.2 Performance Measurement
    4.5 Experimental Results and Analysis
    4.6 Analysis and Comparison
    4.7 Summary
Chapter5 Effective Feature Packet for IM Application At Early Stage Traffic
    5.1 Introduction
    5.2 Data Sets
        5.2.1 HIT Lab Trace Dataset
        5.2.2 HIT Dorm Trace Dataset
    5.3 Proposed Model
    5.4 Methodology
        5.4.1 Machine Learning Classifiers
        5.4.2 Statistical Test
    5.5 Evaluation Criteria for Performance Measurement
    5.6 Results and analysis
    5.7 Mutual Information Results of the HIT Trace I Data Set
        5.7.1 Mutual Information Results of the HIT Trace II Data Set
        5.7.2 Analysis Results of HIT Lab Trace Data Set of the Text Messages
    5.8 Summary
Conclusions
References
详细总结 基于机器学习的即时通信流量分类技术
Published Papers
Acknowledgement
Resume



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