A Big Network Traffic Data Fusion Approach Based on Fisher and Deep Auto-Encoder

被引:20
|
作者
Tao, Xiaoling [1 ,2 ]
Kong, Deyan [1 ]
Wei, Yi [2 ]
Wang, Yong [2 ,3 ]
机构
[1] Guilin Univ Elect Technol, Key Lab Cognit Radio & Informat Proc, Guilin 541004, Peoples R China
[2] Guilin Univ Elect Technol, Guangxi Coll & Univ Key Lab Cloud Comp & Complex, Guilin 541004, Peoples R China
[3] Guilin Univ Elect Technol, Guangxi Key Lab Trusted Software, Guilin 541004, Peoples R China
基金
中国国家自然科学基金;
关键词
big network traffic data; data fusion; Fisher; deep auto-encoder;
D O I
10.3390/info7020020
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页数:10
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