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A comparison study of particale swarm optimizzation and differential evolution for feature selection intrussion detection system

Intrusion detection system (IDS) has become an important system to every company due to the increasing attacks even in such a new ways of attacks for that reason improving intrusion detection system is very important. The importance of feature selection lays on removing noisy, irrelevant and redunda...

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Main Author: Hussein, Mohamud Sheikh Ali
Format: Thesis
Published: 2014
Subjects:
QA Mathematics
Online Access:http://eprints.utm.my/id/eprint/48314/
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spelling my.utm.483142017-08-03T03:31:08Z http://eprints.utm.my/id/eprint/48314/ A comparison study of particale swarm optimizzation and differential evolution for feature selection intrussion detection system Hussein, Mohamud Sheikh Ali QA Mathematics Intrusion detection system (IDS) has become an important system to every company due to the increasing attacks even in such a new ways of attacks for that reason improving intrusion detection system is very important. The importance of feature selection lays on removing noisy, irrelevant and redundant data which can cause overload to the system. IDS faces very large amount of data which consists of many different features. Hence feature selection is used in order to select significant features which reduce unnecessary/noisy data. In feature selection two search algorithms; Particle Swarm Optimization (PSO) and Differential Evolution (DE) is s used to select significant features for the respective five categories of network traffics which are; Normal, Probe, Denial-of-Service (DoS), User-to-Root (U2R) and Remote-to-Local (R2L). Selecting significant features increases the performance of the IDS in terms of detection accuracy. This project aims to compare between two optimization heuristic algorithms PSO and DE for feature selection in IDS. In this project Support Vector Machine (SVM) is used in this study as a classifier. The tool used in this project is Waikato Environment for Knowledge Analysis (WEKA) and visual programming environment. Intrusion detection dataset (KDD Cup 1999) is the data used in this work. The experimental results are described in the end of this work, which shows that PSO is the best for detecting Normal, Probe and DoS 2014 Thesis NonPeerReviewed Hussein, Mohamud Sheikh Ali (2014) A comparison study of particale swarm optimizzation and differential evolution for feature selection intrussion detection system. Masters thesis, Universiti Teknologi Malaysia, Faculty of Computing.
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA Mathematics
spellingShingle QA Mathematics
Hussein, Mohamud Sheikh Ali
A comparison study of particale swarm optimizzation and differential evolution for feature selection intrussion detection system
description Intrusion detection system (IDS) has become an important system to every company due to the increasing attacks even in such a new ways of attacks for that reason improving intrusion detection system is very important. The importance of feature selection lays on removing noisy, irrelevant and redundant data which can cause overload to the system. IDS faces very large amount of data which consists of many different features. Hence feature selection is used in order to select significant features which reduce unnecessary/noisy data. In feature selection two search algorithms; Particle Swarm Optimization (PSO) and Differential Evolution (DE) is s used to select significant features for the respective five categories of network traffics which are; Normal, Probe, Denial-of-Service (DoS), User-to-Root (U2R) and Remote-to-Local (R2L). Selecting significant features increases the performance of the IDS in terms of detection accuracy. This project aims to compare between two optimization heuristic algorithms PSO and DE for feature selection in IDS. In this project Support Vector Machine (SVM) is used in this study as a classifier. The tool used in this project is Waikato Environment for Knowledge Analysis (WEKA) and visual programming environment. Intrusion detection dataset (KDD Cup 1999) is the data used in this work. The experimental results are described in the end of this work, which shows that PSO is the best for detecting Normal, Probe and DoS
format Thesis
author Hussein, Mohamud Sheikh Ali
author_facet Hussein, Mohamud Sheikh Ali
author_sort Hussein, Mohamud Sheikh Ali
title A comparison study of particale swarm optimizzation and differential evolution for feature selection intrussion detection system
title_short A comparison study of particale swarm optimizzation and differential evolution for feature selection intrussion detection system
title_full A comparison study of particale swarm optimizzation and differential evolution for feature selection intrussion detection system
title_fullStr A comparison study of particale swarm optimizzation and differential evolution for feature selection intrussion detection system
title_full_unstemmed A comparison study of particale swarm optimizzation and differential evolution for feature selection intrussion detection system
title_sort comparison study of particale swarm optimizzation and differential evolution for feature selection intrussion detection system
publishDate 2014
url http://eprints.utm.my/id/eprint/48314/
_version_ 1643652524170805248
score 13.211869

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