Features selection for intrusion detection system using hybridize PSO-SVM

An Intrusion Detection System is software or application which is used to detect thread, malicious activities and the unauthorized access to the computer system and warn the administrators by generating alarms. Features selection process can be considered a problem of global combinatorial opti...

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Main Author:	Tabaan, Alaa Abdulrahman
Format:	Thesis
Language:	English
Published:	2016
Subjects:	Intrusion detection systems (Computer security) Support vector machines Computer networks - Security measures
Online Access:	http://psasir.upm.edu.my/id/eprint/67854/1/FSKTM%202017%2022%20IR.pdf
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id	my-upm-ir.67854
record_format	uketd_dc
spelling	my-upm-ir.678542019-03-28T07:07:25Z Features selection for intrusion detection system using hybridize PSO-SVM 2016-12 Tabaan, Alaa Abdulrahman An Intrusion Detection System is software or application which is used to detect thread, malicious activities and the unauthorized access to the computer system and warn the administrators by generating alarms. Features selection process can be considered a problem of global combinatorial optimization in machine learning. Genetic algorithm GA had been adopted to perform features selection method; however, this method could not deliver an acceptable detection rate, lower accuracy, and higher false alarm rates. Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. The results reveal that the proposed hybrid algorithm is capable of achieving classification accuracy values of (95.82 % and 97.68 %), detection rates values of (95.8 % and 99.3 %) and false alarm rates values of (0.083 % and 0.045 %) on both KDD CUP 99 and NSL KDD. Electing the best set of features will help to improve the classifier predictions in terms of the normal and abnormal pattern. The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics. Intrusion detection systems (Computer security) Support vector machines Computer networks - Security measures 2016-12 Thesis http://psasir.upm.edu.my/id/eprint/67854/ http://psasir.upm.edu.my/id/eprint/67854/1/FSKTM%202017%2022%20IR.pdf text en public masters Universiti Putra Malaysia Intrusion detection systems (Computer security) Support vector machines Computer networks - Security measures
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Intrusion detection systems (Computer security) Support vector machines Computer networks - Security measures
spellingShingle	Intrusion detection systems (Computer security) Support vector machines Computer networks - Security measures Tabaan, Alaa Abdulrahman Features selection for intrusion detection system using hybridize PSO-SVM
description	An Intrusion Detection System is software or application which is used to detect thread, malicious activities and the unauthorized access to the computer system and warn the administrators by generating alarms. Features selection process can be considered a problem of global combinatorial optimization in machine learning. Genetic algorithm GA had been adopted to perform features selection method; however, this method could not deliver an acceptable detection rate, lower accuracy, and higher false alarm rates. Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. The results reveal that the proposed hybrid algorithm is capable of achieving classification accuracy values of (95.82 % and 97.68 %), detection rates values of (95.8 % and 99.3 %) and false alarm rates values of (0.083 % and 0.045 %) on both KDD CUP 99 and NSL KDD. Electing the best set of features will help to improve the classifier predictions in terms of the normal and abnormal pattern. The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.
format	Thesis
qualification_level	Master's degree
author	Tabaan, Alaa Abdulrahman
author_facet	Tabaan, Alaa Abdulrahman
author_sort	Tabaan, Alaa Abdulrahman
title	Features selection for intrusion detection system using hybridize PSO-SVM
title_short	Features selection for intrusion detection system using hybridize PSO-SVM
title_full	Features selection for intrusion detection system using hybridize PSO-SVM
title_fullStr	Features selection for intrusion detection system using hybridize PSO-SVM
title_full_unstemmed	Features selection for intrusion detection system using hybridize PSO-SVM
title_sort	features selection for intrusion detection system using hybridize pso-svm
granting_institution	Universiti Putra Malaysia
publishDate	2016
url	http://psasir.upm.edu.my/id/eprint/67854/1/FSKTM%202017%2022%20IR.pdf
_version_	1747812526953857024

Features selection for intrusion detection system using hybridize PSO-SVM

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