基于Fisher和深度自编码器的大网络流量数据融合方法

Tao Xiao-ling, Kong De-yan, Wei Yi, Wang Yong
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引用次数: 15

摘要

为了减少输入空间,通常在分类之前进行数据融合。这些降维技术有助于降低分类模型的复杂性,从而提高分类性能。传统的监督方法需要标记样本,而当前的网络流量数据大多没有标记。因此,通过同时使用标记和未标记数据,而不是单独使用每一个数据,可以构建更好的学习器。为了降低数据维数和计算复杂度,提出了一种基于Fisher和深度自编码器(DFA-F-DAE)的网络流量数据融合方法。实验结果表明,DFA-F-DAE通过数据降维提高了J48、bp神经网络(BPNN)和支持向量机(SVM)三种分类算法的泛化能力。我们发现DFA-F-DAE显著提高了大网络流量分类的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Big Network Traffic Data Fusion Approach Based on Fisher and Deep Auto-Encoder
Data fusion is usually performed prior to classification in order to reduce the input space. These dimensionality reduction techniques help to decline the complexity of the classification model and thus improve the classification performance. The traditional supervised methods demand labeled samples, and the current network traffic data mostly is not labeled. Thereby, better learners will be built by using both labeled and unlabeled data, than using each one alone. In this paper, a novel network traffic data fusion approach based on Fisher and deep auto-encoder (DFA-F-DAE) is proposed to reduce the data dimensions and the complexity of computation. The experimental results show that the DFA-F-DAE improves the generalization ability of the three classification algorithms (J48, back propagation neural network (BPNN), and support vector machine (SVM)) by data dimensionality reduction. We found that the DFA-F-DAE remarkably improves the efficiency of big network traffic classification.
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