On Extracting the Spatial-Temporal Features of Network Traffic Patterns: A Tensor Based Deep Learning Model

Fengxiao Tang, Bomin Mao, Zubair Md Fadlullah, Jiajia Liu, Nei Kato

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Recently, the Artificial Intelligence (AI) technology is widely employed in both academia and industry. Few researches on employing deep learning for network traffic control and wireless resource management have emerged as a new research direction in the communication networking area. However, how to deploy the deep learning structure in a universal way to suit for common network applications and how to format the training data still remain as a formidable research challenge. Furthermore, whether and why the deep learning structure is efficient in contrast with that of the shallow learning model, from the perspective of networking applications, has not been investigated well in the literature. In this paper, we address these issues, and propose a matrix and tensor based spatial-temporal training data format. Our proposal can be regarded as a universal characterization of network traffic patterns. Furthermore, a deep Convolutional Neural Network (CNN) structure is constructed to fit the proposed training data format of the corresponding tensor space. The performance of our envisioned tensor based deep learning model is further analyzed by comparing with the shallow learning model. Computer based simulation results demonstrate that our proposal achieves significant improvement in terms of both training accuracy and network performance.

Original languageEnglish
Title of host publicationProceedings of 2018 6th IEEE International Conference on Network Infrastructure and Digital Content, IC-NIDC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages445-451
Number of pages7
ISBN (Electronic)9781538660669
DOIs
Publication statusPublished - 2018 Nov 6
Event6th IEEE International Conference on Network Infrastructure and Digital Content, IC-NIDC 2018 - Guiyang, China
Duration: 2018 Aug 222018 Aug 24

Publication series

NameProceedings of 2018 6th IEEE International Conference on Network Infrastructure and Digital Content, IC-NIDC 2018

Other

Other6th IEEE International Conference on Network Infrastructure and Digital Content, IC-NIDC 2018
CountryChina
CityGuiyang
Period18/8/2218/8/24

Keywords

  • Convolutional Neural Network (CNN)
  • Deep learning
  • network traffic control
  • tensor

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture

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