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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Main Author: Rugayyah Alhaddad, Syamira
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/82147
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:82147
spelling 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
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
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
_version_ 1822282132158939136