Name
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/IMCOM48794.2020.9001712 | 2020 14th International Conference on Ubiquitous Information Management and Communication (IMCOM) |
Keywords | Field | DocType |
Network traffic,prediction,deep learning,convolutional neural networks | Data mining,F1 score,Computer science,Convolutional neural network,Internet protocol suite,Real-time computing,Artificial intelligence,Deep learning,Traffic prediction,Random forest,Management system,Python (programming language) | Conference |
ISSN | ISBN | Citations |
2644-0164 | 978-1-7281-5454-1 | 0 |
PageRank | References | Authors |
0.34 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taejin Ko | 1 | 0 | 0.34 |
| Syed M. Raza | 2 | 18 | 8.68 |
| Thien-Binh Dang | 3 | 0 | 0.34 |
| Moonseong Kim | 4 | 143 | 39.75 |
| Hyunseung Choo | 5 | 1364 | 195.25 |