Computer network traffic classification using machine learning technique
Other Title(s)
تصنيف حركة المرور في شبكات الحاسوب باستخدام تقنية تعلم الآلة
Dissertant
Thesis advisor
al-Kasasibah, Muhammad Sharari Zamil
Comitee Members
al-Abbadi, Muhammad Ali Husayn
al-Mahadin, Bassam
al-Qatawinah, Jafar Salih
University
Mutah University
Faculty
Information Technology College
University Country
Jordan
Degree
Master
Degree Date
2015
English Abstract
In recent years, the uses of the Internet has increased and been extensively developed.
Many modern applications have evolved to facilitate the process of social communication.
Also the traffic classification process has appeared as a science in itself on the Internet nowadays.
In this thesis, we generate a new dataset and tested it through four Machine Learning (ML) algorithms: Adaptive Boosting (meta.Adaboost (j48)), Random Forest, J48 and MultiLayer Perceptron (MLP).
Additionally, we separated the classification process into two cases: Non- Voice Over Internet Protocol (Non-VOIP) and Voice Over Internet Protocol (VOIP) applications, the second one is called Multiclasses, which contains five applications (classes), namely: PayPal, YouTube, Google talk (Gtalk), Yahoo Messenger and Skype.
We choose these applications from the Transport Layer (TL).
The generated dataset was compiled by means of a different process that included: packet capturing, features extraction and classification processes, we using also four statistical features.
The dataset used here contains real data from a live network using an experimental testbed from experimental environment within a campus environment.
In the both cases: Non-VOIP and VOIP case and Multiclasses classification case, the meta.Adaboost (j48) classifier achieved the highest accuracy level among other classifiers, of 98.6605% and 98.3007% respectively.
The J48 classifier achieved the minimum time for building the training model in the two cases of classification.
Also, the MLP took the maximum time between other classifiers for build the training model in both cases.
Main Subjects
Information Technology and Computer Science
No. of Pages
64
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Literature review.
Chapter Three : Design and methodology.
Chapter Four : Experiments, discussion and recommendations.
References.
American Psychological Association (APA)
Abu Samhadanh, Nasibah Hamdan. (2015). Computer network traffic classification using machine learning technique. (Master's theses Theses and Dissertations Master). Mutah University, Jordan
https://search.emarefa.net/detail/BIM-729761
Modern Language Association (MLA)
Abu Samhadanh, Nasibah Hamdan. Computer network traffic classification using machine learning technique. (Master's theses Theses and Dissertations Master). Mutah University. (2015).
https://search.emarefa.net/detail/BIM-729761
American Medical Association (AMA)
Abu Samhadanh, Nasibah Hamdan. (2015). Computer network traffic classification using machine learning technique. (Master's theses Theses and Dissertations Master). Mutah University, Jordan
https://search.emarefa.net/detail/BIM-729761
Language
English
Data Type
Arab Theses
Record ID
BIM-729761
