Network Prediction with Traffic Gradient Classification using Convolutional Neural Networks

Taejin Ko; Syed M. Raza; Dang Thien Binh; Moonseong Kim; Hyunseung Choo

doi:10.1109/IMCOM48794.2020.9001712

Network Prediction with Traffic Gradient Classification using Convolutional Neural Networks

Taejin Ko
, Syed M. Raza
, Dang Thien Binh
, Moonseong Kim
, Hyunseung Choo

Department of Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

23 Scopus citations

Abstract

Current TCP/IP network infrastructures and management systems are facing a tough time in handling the unusual increase in network traffic due to the surge of typical real-time applications. To solve this problem, management system predicts the changes in network traffic and handle them proactively. In this paper, we convert the traffic prediction into a classification problem and use Convolutional Neural Network (CNN) deep-learning technique to classify the fixed time interval traffic into different classes. We implement the CNN model using Python and Keras library. The proposed algorithm shows higher accuracy (92.6%) and F1 score than the existing Random Forest machine learning method.

Original language	English
Title of host publication	Proceedings of the 2020 14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020
Editors	Sukhan Lee, Hyunseung Choo, Roslan Ismail
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728154534
DOIs	https://doi.org/10.1109/IMCOM48794.2020.9001712
State	Published - Jan 2020
Event	14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020 - Taichung, Taiwan, Province of China Duration: 3 Jan 2020 → 5 Jan 2020

Publication series

Name	Proceedings of the 2020 14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020

Conference

Conference	14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020
Country/Territory	Taiwan, Province of China
City	Taichung
Period	3/01/20 → 5/01/20

Keywords

convolutional neural networks
deep learning
Network traffic
prediction

Access to Document

10.1109/IMCOM48794.2020.9001712

Cite this

Ko, T., Raza, S. M., Binh, D. T., Kim, M., & Choo, H. (2020). Network Prediction with Traffic Gradient Classification using Convolutional Neural Networks. In S. Lee, H. Choo, & R. Ismail (Eds.), Proceedings of the 2020 14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020 Article 9001712 (Proceedings of the 2020 14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IMCOM48794.2020.9001712

Ko, Taejin ; Raza, Syed M. ; Binh, Dang Thien et al. / Network Prediction with Traffic Gradient Classification using Convolutional Neural Networks. Proceedings of the 2020 14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020. editor / Sukhan Lee ; Hyunseung Choo ; Roslan Ismail. Institute of Electrical and Electronics Engineers Inc., 2020. (Proceedings of the 2020 14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020).

@inproceedings{135572ec915e45ba864eebe77508e5a3,

title = "Network Prediction with Traffic Gradient Classification using Convolutional Neural Networks",

abstract = "Current TCP/IP network infrastructures and management systems are facing a tough time in handling the unusual increase in network traffic due to the surge of typical real-time applications. To solve this problem, management system predicts the changes in network traffic and handle them proactively. In this paper, we convert the traffic prediction into a classification problem and use Convolutional Neural Network (CNN) deep-learning technique to classify the fixed time interval traffic into different classes. We implement the CNN model using Python and Keras library. The proposed algorithm shows higher accuracy (92.6\%) and F1 score than the existing Random Forest machine learning method.",

keywords = "convolutional neural networks, deep learning, Network traffic, prediction",

author = "Taejin Ko and Raza, \{Syed M.\} and Binh, \{Dang Thien\} and Moonseong Kim and Hyunseung Choo",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020 ; Conference date: 03-01-2020 Through 05-01-2020",

year = "2020",

month = jan,

doi = "10.1109/IMCOM48794.2020.9001712",

language = "English",

series = "Proceedings of the 2020 14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "Sukhan Lee and Hyunseung Choo and Roslan Ismail",

booktitle = "Proceedings of the 2020 14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020",

}

Ko, T, Raza, SM, Binh, DT, Kim, M & Choo, H 2020, Network Prediction with Traffic Gradient Classification using Convolutional Neural Networks. in S Lee, H Choo & R Ismail (eds), Proceedings of the 2020 14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020., 9001712, Proceedings of the 2020 14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020, Institute of Electrical and Electronics Engineers Inc., 14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020, Taichung, Taiwan, Province of China, 3/01/20. https://doi.org/10.1109/IMCOM48794.2020.9001712

Network Prediction with Traffic Gradient Classification using Convolutional Neural Networks. / Ko, Taejin; Raza, Syed M.; Binh, Dang Thien et al.
Proceedings of the 2020 14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020. ed. / Sukhan Lee; Hyunseung Choo; Roslan Ismail. Institute of Electrical and Electronics Engineers Inc., 2020. 9001712 (Proceedings of the 2020 14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Network Prediction with Traffic Gradient Classification using Convolutional Neural Networks

AU - Ko, Taejin

AU - Raza, Syed M.

AU - Binh, Dang Thien

AU - Kim, Moonseong

AU - Choo, Hyunseung

PY - 2020/1

Y1 - 2020/1

N2 - Current TCP/IP network infrastructures and management systems are facing a tough time in handling the unusual increase in network traffic due to the surge of typical real-time applications. To solve this problem, management system predicts the changes in network traffic and handle them proactively. In this paper, we convert the traffic prediction into a classification problem and use Convolutional Neural Network (CNN) deep-learning technique to classify the fixed time interval traffic into different classes. We implement the CNN model using Python and Keras library. The proposed algorithm shows higher accuracy (92.6%) and F1 score than the existing Random Forest machine learning method.

AB - Current TCP/IP network infrastructures and management systems are facing a tough time in handling the unusual increase in network traffic due to the surge of typical real-time applications. To solve this problem, management system predicts the changes in network traffic and handle them proactively. In this paper, we convert the traffic prediction into a classification problem and use Convolutional Neural Network (CNN) deep-learning technique to classify the fixed time interval traffic into different classes. We implement the CNN model using Python and Keras library. The proposed algorithm shows higher accuracy (92.6%) and F1 score than the existing Random Forest machine learning method.

KW - convolutional neural networks

KW - deep learning

KW - Network traffic

KW - prediction

UR - https://www.scopus.com/pages/publications/85081138380

U2 - 10.1109/IMCOM48794.2020.9001712

DO - 10.1109/IMCOM48794.2020.9001712

M3 - Conference contribution

AN - SCOPUS:85081138380

T3 - Proceedings of the 2020 14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020

BT - Proceedings of the 2020 14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020

A2 - Lee, Sukhan

A2 - Choo, Hyunseung

A2 - Ismail, Roslan

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020

Y2 - 3 January 2020 through 5 January 2020

ER -

Ko T, Raza SM, Binh DT, Kim M, Choo H. Network Prediction with Traffic Gradient Classification using Convolutional Neural Networks. In Lee S, Choo H, Ismail R, editors, Proceedings of the 2020 14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9001712. (Proceedings of the 2020 14th International Conference on Ubiquitous Information Management and Communication, IMCOM 2020). doi: 10.1109/IMCOM48794.2020.9001712

Network Prediction with Traffic Gradient Classification using Convolutional Neural Networks

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this