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A New Method of Improving the Traditional Traffic Identification and Accuracy Cover

A New Method of Improving the Traditional Traffic Identification and Accuracy

International Journal of Advanced Network, Monitoring and Controls
Volume 3 (2018): Issue 3 (January 2018)
By: Wang Zhongsheng and  Gao Jiaqiong  
Open Access
|Oct 2019

Figures & Tables

Figure 1.

Classification framework based on SVM
Classification framework based on SVM

Figure 2.

Comparison of different models
Comparison of different models

Figure 3.

Comparison of stability between Bayesian and SVM
Comparison of stability between Bayesian and SVM

COLLECTED DATA SAMPLES

DataSetTime (ms)Total flow
DataSetA1hour2300
DataSetB1 hour3020
DataSetC1 hour1831
DataSetD1 hour2290
DataSetE1 day9538
DOI: https://doi.org/10.21307/ijanmc-2019-006 | Journal eISSN: 2470-8038
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Language: English
Page range: 53 - 60
Published on: Oct 1, 2019
Published by: Xi’an Technological University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Support Vector Machine (SVM),
Traffic Classification,
Feature Extraction,
Kernel Function
Related subjects:
Computer sciences,
Computer sciences, other

© 2019 Wang Zhongsheng, Gao Jiaqiong, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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