5G NETWORK TRAFFIC CLASSIFICATION SYSTEM USING STACKING MACHINE LEARNING BASED ON NETWORK MONITORING TOOLS DATA
The 5G network requires a traffic management system capable of accurately classifying various types of application and service traffic. Protocol behavior- based classification systems often fail to handle this complexity, resulting in decreased network performance and a subpar user experience...
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Online Access: | https://digilib.itb.ac.id/gdl/view/82147 |
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id-itb.:821472024-07-05T18:29:17Z5G NETWORK TRAFFIC CLASSIFICATION SYSTEM USING STACKING MACHINE LEARNING BASED ON NETWORK MONITORING TOOLS DATA Rugayyah Alhaddad, Syamira Indonesia Final Project traffic classification, machine learning, 5G network traffic classification, stacking, stacking machine learning. INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/82147 The 5G network requires a traffic management system capable of accurately classifying various types of application and service traffic. Protocol behavior- based classification systems often fail to handle this complexity, resulting in decreased network performance and a subpar user experience. To address this issue, the author designed a 5G network traffic classification system using stacking machine learning to improve the accuracy and efficiency of traffic classification. In this context, the machine learning method employed is stacking machine learning, which includes k-Nearest Neighbor (kNN) and Support Vector Machine (SVM), as well as a stacking model that incorporates kNN, SVM, and Random Forest. These stacking models will be trained using data obtained from network monitoring tools to create a comprehensive 5G traffic classification system, which will be displayed on a website through an interactive dashboard. On this website, users can choose whether they only want to see visualizations regarding data traffic classification or use the website as a tool to view 5G traffic classification results using the user's network traffic data which is uploaded to the website. The system's classification results will include traffic types such as video, game, and voice. Testing conducted on each stacking machine learning model demonstrated an accuracy of 99.9% for each model, indicating that this system effectively optimizes traffic grouping based on its characteristics. text |
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description |
The 5G network requires a traffic management system capable of accurately
classifying various types of application and service traffic. Protocol behavior-
based classification systems often fail to handle this complexity, resulting in
decreased network performance and a subpar user experience. To address this
issue, the author designed a 5G network traffic classification system using stacking
machine learning to improve the accuracy and efficiency of traffic classification.
In this context, the machine learning method employed is stacking machine
learning, which includes k-Nearest Neighbor (kNN) and Support Vector Machine
(SVM), as well as a stacking model that incorporates kNN, SVM, and Random
Forest. These stacking models will be trained using data obtained from network
monitoring tools to create a comprehensive 5G traffic classification system, which
will be displayed on a website through an interactive dashboard. On this website,
users can choose whether they only want to see visualizations regarding data traffic
classification or use the website as a tool to view 5G traffic classification results
using the user's network traffic data which is uploaded to the website. The system's
classification results will include traffic types such as video, game, and voice.
Testing conducted on each stacking machine learning model demonstrated an
accuracy of 99.9% for each model, indicating that this system effectively optimizes
traffic grouping based on its characteristics. |
format |
Final Project |
author |
Rugayyah Alhaddad, Syamira |
spellingShingle |
Rugayyah Alhaddad, Syamira 5G NETWORK TRAFFIC CLASSIFICATION SYSTEM USING STACKING MACHINE LEARNING BASED ON NETWORK MONITORING TOOLS DATA |
author_facet |
Rugayyah Alhaddad, Syamira |
author_sort |
Rugayyah Alhaddad, Syamira |
title |
5G NETWORK TRAFFIC CLASSIFICATION SYSTEM USING STACKING MACHINE LEARNING BASED ON NETWORK MONITORING TOOLS DATA |
title_short |
5G NETWORK TRAFFIC CLASSIFICATION SYSTEM USING STACKING MACHINE LEARNING BASED ON NETWORK MONITORING TOOLS DATA |
title_full |
5G NETWORK TRAFFIC CLASSIFICATION SYSTEM USING STACKING MACHINE LEARNING BASED ON NETWORK MONITORING TOOLS DATA |
title_fullStr |
5G NETWORK TRAFFIC CLASSIFICATION SYSTEM USING STACKING MACHINE LEARNING BASED ON NETWORK MONITORING TOOLS DATA |
title_full_unstemmed |
5G NETWORK TRAFFIC CLASSIFICATION SYSTEM USING STACKING MACHINE LEARNING BASED ON NETWORK MONITORING TOOLS DATA |
title_sort |
5g network traffic classification system using stacking machine learning based on network monitoring tools data |
url |
https://digilib.itb.ac.id/gdl/view/82147 |
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1822282132158939136 |