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Information 2016, 7(2), 20; doi:10.3390/info7020020

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

1,2,* , 1
,
2,* and 2,3
1
Key Laboratory of Cognitive Radio and Information Processing, Guilin University of Electronic Technology, Guilin 541004, China
2
Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems, Guilin University of Electronic Technology, Guilin 541004, China
3
Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Yong Yu, Yu Wang and Willy Susilo
Received: 27 January 2016 / Revised: 6 March 2016 / Accepted: 7 March 2016 / Published: 23 March 2016
(This article belongs to the Special Issue Recent Advances of Big Data Technology)
View Full-Text   |   Download PDF [809 KB, uploaded 23 March 2016]   |  

Abstract

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. View Full-Text
Keywords: big network traffic data; data fusion; Fisher; deep auto-encoder big network traffic data; data fusion; Fisher; deep auto-encoder
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Tao, X.; Kong, D.; Wei, Y.; Wang, Y. A Big Network Traffic Data Fusion Approach Based on Fisher and Deep Auto-Encoder. Information 2016, 7, 20.

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