Enhanced grey wolf optimisation algorithm for feature selection in anomaly detection

Anomaly detection deals with identification of items that do not conform to an expected pattern or items present in a dataset. The performance of different mechanisms utilized to perform the anomaly detection depends heavily on the group of features used. Thus, not all features in the dataset can be...

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Main Author:	Almazini, Hussein
Format:	Thesis
Language:	eng eng
Published:	2022
Subjects:	QA Mathematics
Online Access:	https://etd.uum.edu.my/10192/1/s903692_01.pdf https://etd.uum.edu.my/10192/2/s903692_02.pdf
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id	my-uum-etd.10192
record_format	uketd_dc
spelling	my-uum-etd.101922023-01-04T05:11:31Z Enhanced grey wolf optimisation algorithm for feature selection in anomaly detection 2022 Almazini, Hussein Ku Mahamud, Ku Ruhana Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts & Sciences QA Mathematics Anomaly detection deals with identification of items that do not conform to an expected pattern or items present in a dataset. The performance of different mechanisms utilized to perform the anomaly detection depends heavily on the group of features used. Thus, not all features in the dataset can be used in the classification process since some features may lead to low performance of classifier. Feature selection (FS) is a good mechanism that minimises the dimension of high-dimensional datasets by deleting the irrelevant features. Modified Binary Grey Wolf Optimiser (MBGWO) is a modern metaheuristic algorithm that has successfully been used for FS for anomaly detection. However, the MBGWO has several issues in finding a good quality solution. Thus, this study proposes an enhanced binary grey wolf optimiser (EBGWO) algorithm for FS in anomaly detection to overcome the algorithm issues. The first modification enhances the initial population of the MBGWO using a heuristic based Ant Colony Optimisation algorithm. The second modification develops a new position update mechanism using the Bat Algorithm movement. The third modification improves the controlled parameter of the MBGWO algorithm using indicators from the search process to refine the solution. The EBGWO algorithm was evaluated on NSL-KDD and six (6) benchmark datasets from the University California Irvine (UCI) repository against ten (10) benchmark metaheuristic algorithms. Experimental results of the EBGWO algorithm on the NSL-KDD dataset in terms of number of selected features and classification accuracy are superior to other benchmark optimisation algorithms. Moreover, experiments on the six (6) UCI datasets showed that the EBGWO algorithm is superior to the benchmark algorithms in terms of classification accuracy and second best for the number of selected features. The proposed EBGWO algorithm can be used for FS in anomaly detection tasks that involve any dataset size from various application domains. 2022 Thesis https://etd.uum.edu.my/10192/ https://etd.uum.edu.my/10192/1/s903692_01.pdf text eng 2025-05-17 staffonly https://etd.uum.edu.my/10192/2/s903692_02.pdf text eng public other doctoral Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Ku Mahamud, Ku Ruhana
topic	QA Mathematics
spellingShingle	QA Mathematics Almazini, Hussein Enhanced grey wolf optimisation algorithm for feature selection in anomaly detection
description	Anomaly detection deals with identification of items that do not conform to an expected pattern or items present in a dataset. The performance of different mechanisms utilized to perform the anomaly detection depends heavily on the group of features used. Thus, not all features in the dataset can be used in the classification process since some features may lead to low performance of classifier. Feature selection (FS) is a good mechanism that minimises the dimension of high-dimensional datasets by deleting the irrelevant features. Modified Binary Grey Wolf Optimiser (MBGWO) is a modern metaheuristic algorithm that has successfully been used for FS for anomaly detection. However, the MBGWO has several issues in finding a good quality solution. Thus, this study proposes an enhanced binary grey wolf optimiser (EBGWO) algorithm for FS in anomaly detection to overcome the algorithm issues. The first modification enhances the initial population of the MBGWO using a heuristic based Ant Colony Optimisation algorithm. The second modification develops a new position update mechanism using the Bat Algorithm movement. The third modification improves the controlled parameter of the MBGWO algorithm using indicators from the search process to refine the solution. The EBGWO algorithm was evaluated on NSL-KDD and six (6) benchmark datasets from the University California Irvine (UCI) repository against ten (10) benchmark metaheuristic algorithms. Experimental results of the EBGWO algorithm on the NSL-KDD dataset in terms of number of selected features and classification accuracy are superior to other benchmark optimisation algorithms. Moreover, experiments on the six (6) UCI datasets showed that the EBGWO algorithm is superior to the benchmark algorithms in terms of classification accuracy and second best for the number of selected features. The proposed EBGWO algorithm can be used for FS in anomaly detection tasks that involve any dataset size from various application domains.
format	Thesis
qualification_name	other
qualification_level	Doctorate
author	Almazini, Hussein
author_facet	Almazini, Hussein
author_sort	Almazini, Hussein
title	Enhanced grey wolf optimisation algorithm for feature selection in anomaly detection
title_short	Enhanced grey wolf optimisation algorithm for feature selection in anomaly detection
title_full	Enhanced grey wolf optimisation algorithm for feature selection in anomaly detection
title_fullStr	Enhanced grey wolf optimisation algorithm for feature selection in anomaly detection
title_full_unstemmed	Enhanced grey wolf optimisation algorithm for feature selection in anomaly detection
title_sort	enhanced grey wolf optimisation algorithm for feature selection in anomaly detection
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2022
url	https://etd.uum.edu.my/10192/1/s903692_01.pdf https://etd.uum.edu.my/10192/2/s903692_02.pdf
_version_	1776103762592530432

Enhanced grey wolf optimisation algorithm for feature selection in anomaly detection

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