Network Prediction with Traffic Gradient Classification using Convolutional Neural Networks

被引：11

作者：

Ko, Taejin ^{[1
]}

Raza, Syed M. ^{[1
]}

Dang Thien Binh ^{[1
]}

Kim, Moonseong ^{[2
]}

Choo, Hyunseung ^{[1
]}

机构：

[1] Sungkyunkwan Univ, Suwon, South Korea

[2] Seoul Theol Univ, Bucheon, South Korea

来源：

PROCEEDINGS OF THE 2020 14TH INTERNATIONAL CONFERENCE ON UBIQUITOUS INFORMATION MANAGEMENT AND COMMUNICATION (IMCOM) | 2020年

关键词：

Network traffic; prediction; deep learning; convolutional neural networks;

D O I：

10.1109/imcom48794.2020.9001712

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：4

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