A Comparative Study of Machine Learning-based Approach for Network Traffic Classification
Internet usage has increased rapidly and become an essential part of human life, corresponding to the rapid development of network infrastructure in recent years. Thus, protecting users’ confidential information when joining the global network becomes one of the most significant considerations. Even...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Negeri Malang
2022-01-01
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| Series: | Knowledge Engineering and Data Science |
| Online Access: | http://journal2.um.ac.id/index.php/keds/article/view/25393 |
| _version_ | 1798033934072676352 |
|---|---|
| author | Kien Trang An Hoang Nguyen |
| author_facet | Kien Trang An Hoang Nguyen |
| author_sort | Kien Trang |
| collection | DOAJ |
| description | Internet usage has increased rapidly and become an essential part of human life, corresponding to the rapid development of network infrastructure in recent years. Thus, protecting users’ confidential information when joining the global network becomes one of the most significant considerations. Even though multiple encryption algorithms and techniques have been applied in different parties, including internet providers, and web hosting, this situation also allows the hacker to attack the network system anonymously. Therefore, the significance of classifying network data streams to improve network system quality and security is attracting increasing study interests. This work introduces a machine learning-based approach to find the most suitable training model for network traffic classification tasks. Data pre-processing is first applied to normalize each feature type in the dataset. Different machine learning techniques, including k-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Random Forest (RF), are applied based on the normalized features in the classification phase. An open-access dataset ISCXVPN2016 is applied for this research, which includes two types of encryption (VPN and Non-VPN) and seven classes of traffic categories classes. Experimental results on the open dataset have shown that the proposed models have reached a high classification rate – over 85% in some cases, in which the RF model obtains the most refined results among the three techniques. |
| first_indexed | 2024-04-11T20:37:17Z |
| format | Article |
| id | doaj.art-b56bae9035034fb798131dad8c07198d |
| institution | Directory Open Access Journal |
| issn | 2597-4602 2597-4637 |
| language | English |
| last_indexed | 2024-04-11T20:37:17Z |
| publishDate | 2022-01-01 |
| publisher | Universitas Negeri Malang |
| record_format | Article |
| series | Knowledge Engineering and Data Science |
| spelling | doaj.art-b56bae9035034fb798131dad8c07198d2022-12-22T04:04:20ZengUniversitas Negeri MalangKnowledge Engineering and Data Science2597-46022597-46372022-01-014212813710.17977/um018v4i22021p128-1378221A Comparative Study of Machine Learning-based Approach for Network Traffic ClassificationKien Trang0An Hoang Nguyen1School of Electrical Engineering, International University, Ho Chi Minh City, VietnamSchool of Electrical Engineering, International University, Ho Chi Minh City, VietnamInternet usage has increased rapidly and become an essential part of human life, corresponding to the rapid development of network infrastructure in recent years. Thus, protecting users’ confidential information when joining the global network becomes one of the most significant considerations. Even though multiple encryption algorithms and techniques have been applied in different parties, including internet providers, and web hosting, this situation also allows the hacker to attack the network system anonymously. Therefore, the significance of classifying network data streams to improve network system quality and security is attracting increasing study interests. This work introduces a machine learning-based approach to find the most suitable training model for network traffic classification tasks. Data pre-processing is first applied to normalize each feature type in the dataset. Different machine learning techniques, including k-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Random Forest (RF), are applied based on the normalized features in the classification phase. An open-access dataset ISCXVPN2016 is applied for this research, which includes two types of encryption (VPN and Non-VPN) and seven classes of traffic categories classes. Experimental results on the open dataset have shown that the proposed models have reached a high classification rate – over 85% in some cases, in which the RF model obtains the most refined results among the three techniques.http://journal2.um.ac.id/index.php/keds/article/view/25393 |
| spellingShingle | Kien Trang An Hoang Nguyen A Comparative Study of Machine Learning-based Approach for Network Traffic Classification Knowledge Engineering and Data Science |
| title | A Comparative Study of Machine Learning-based Approach for Network Traffic Classification |
| title_full | A Comparative Study of Machine Learning-based Approach for Network Traffic Classification |
| title_fullStr | A Comparative Study of Machine Learning-based Approach for Network Traffic Classification |
| title_full_unstemmed | A Comparative Study of Machine Learning-based Approach for Network Traffic Classification |
| title_short | A Comparative Study of Machine Learning-based Approach for Network Traffic Classification |
| title_sort | comparative study of machine learning based approach for network traffic classification |
| url | http://journal2.um.ac.id/index.php/keds/article/view/25393 |
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