Network traffic classification based on ensemble learning and co-training
Network traffic classification based on ensemble learning and co-training
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DOI：
10.1007/s11432-009-0050-8
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发表时间：
2009-01
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影响因子：
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通讯作者：
Haitao He;Xiaonan Luo;F. Ma;Chunhui Che;Jianmin Wang
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文献类型：
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作者：
Haitao He;Xiaonan Luo;F. Ma;Chunhui Che;Jianmin Wang
网络流量的分类是许多网络研究的基本步骤。然而，随着互联网应用的快速发展，基于端口或基于有效载荷的识别方法的有效性已大大降低，在最近几年。许多研究人员开始将他们的注意力转向另一种基于机器学习的方法。本文提出了一种新的基于机器学习的分类模型，它结合集成学习范式和协同训练技术。与以往的方法相比，该方法采用多分类器和半监督学习相结合的方法，主要克服了流分类准确率有限、适应性差和对标记训练集需求量大的缺点。本文从IP流的数据包级轨迹中提取统计特征，建立特征集，建立分类模型，并对模型进行测试，实验结果证明了其可行性和有效性。
Classification of network traffic is the essential step for many network researches. However, with the rapid evolution of Internet applications the effectiveness of the port-based or payload-based identification approaches has been greatly diminished in recent years. And many researchers begin to turn their attentions to an alternative machine learning based method. This paper presents a novel machine learning-based classification model, which combines ensemble learning paradigm with co-training techniques. Compared to previous approaches, most of which only employed single classifier, multiple classifiers and semi-supervised learning are applied in our method and it mainly helps to overcome three shortcomings: limited flow accuracy rate, weak adaptability and huge demand of labeled training set. In this paper, statistical characteristics of IP flows are extracted from the packet level traces to establish the feature set, then the classification model is created and tested and the empirical results prove its feasibility and effectiveness.