An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection

The biological immune system (BIS) is characterized by networks of cells, tissues, and organs communicating and working in synchronization. It also has the ability to learn, recognize, and remember, thus providing the solid foundation for the development of Artificial Immune System (AIS). Since t...

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Main Author:	Ayodele Nojeem, Lasisi
Format:	Thesis
Language:	English English English
Published:	2018
Subjects:	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
Online Access:	http://eprints.uthm.edu.my/202/1/24p%20LASISI%20AYODELE%20NOJEEM.pdf http://eprints.uthm.edu.my/202/2/LASISI%20AYODELE%20NOJEEM%20WATERMARK.pdf http://eprints.uthm.edu.my/202/3/LASISI%20AYODELE%20NOJEEM%20COPYRIGHT%20DECLARATION.pdf
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spelling	my-uthm-ep.2022021-07-06T08:12:28Z An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection 2018-03 Ayodele Nojeem, Lasisi TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television The biological immune system (BIS) is characterized by networks of cells, tissues, and organs communicating and working in synchronization. It also has the ability to learn, recognize, and remember, thus providing the solid foundation for the development of Artificial Immune System (AIS). Since the emergence of AIS, it has proved itself as an area of computational intelligence. Real-Valued Negative Selection Algorithm with Variable-Sized Detectors (V-Detectors) is an offspring of AIS and demonstrated its potentials in the field of anomaly detection. The V-Detectors algorithm depends greatly on the random detectors generated in monitoring the status of a system. These randomly generated detectors suffer from not been able to adequately cover the non-self space, which diminishes the detection performance of the V-Detectors algorithm. This research therefore proposed CSDE-V-Detectors which entail the use of the hybridization of Cuckoo Search (CS) and Differential Evolution (DE) in optimizing the random detectors of the V-Detectors. The DE is integrated with CS at the population initialization by distributing the population linearly. This linear distribution gives the population a unique, stable, and progressive distribution process. Thus, each individual detector is characteristically different from the other detectors. CSDE capabilities of global search, and use of L´evy flight facilitates the effectiveness of the detector set in the search space. In comparison with V-Detectors, cuckoo search, differential evolution, support vector machine, artificial neural network, na¨ıve bayes, and k-NN, experimental results demonstrates that CSDE-V-Detectors outperforms other algorithms with an average detection rate of 95:30% on all the datasets. This signifies that CSDE-V-Detectors can efficiently attain highest detection rates and lowest false alarm rates for anomaly detection. Thus, the optimization of the randomly detectors of V-Detectors algorithm with CSDE is proficient and suitable for anomaly detection tasks. 2018-03 Thesis http://eprints.uthm.edu.my/202/ http://eprints.uthm.edu.my/202/1/24p%20LASISI%20AYODELE%20NOJEEM.pdf text en public http://eprints.uthm.edu.my/202/2/LASISI%20AYODELE%20NOJEEM%20WATERMARK.pdf text en validuser http://eprints.uthm.edu.my/202/3/LASISI%20AYODELE%20NOJEEM%20COPYRIGHT%20DECLARATION.pdf text en staffonly phd doctoral Universiti Tun Hussein Onn Malaysia Fakulti Sains Komputer dan Teknologi Maklumat
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
spellingShingle	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television Ayodele Nojeem, Lasisi An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection
description	The biological immune system (BIS) is characterized by networks of cells, tissues, and organs communicating and working in synchronization. It also has the ability to learn, recognize, and remember, thus providing the solid foundation for the development of Artificial Immune System (AIS). Since the emergence of AIS, it has proved itself as an area of computational intelligence. Real-Valued Negative Selection Algorithm with Variable-Sized Detectors (V-Detectors) is an offspring of AIS and demonstrated its potentials in the field of anomaly detection. The V-Detectors algorithm depends greatly on the random detectors generated in monitoring the status of a system. These randomly generated detectors suffer from not been able to adequately cover the non-self space, which diminishes the detection performance of the V-Detectors algorithm. This research therefore proposed CSDE-V-Detectors which entail the use of the hybridization of Cuckoo Search (CS) and Differential Evolution (DE) in optimizing the random detectors of the V-Detectors. The DE is integrated with CS at the population initialization by distributing the population linearly. This linear distribution gives the population a unique, stable, and progressive distribution process. Thus, each individual detector is characteristically different from the other detectors. CSDE capabilities of global search, and use of L´evy flight facilitates the effectiveness of the detector set in the search space. In comparison with V-Detectors, cuckoo search, differential evolution, support vector machine, artificial neural network, na¨ıve bayes, and k-NN, experimental results demonstrates that CSDE-V-Detectors outperforms other algorithms with an average detection rate of 95:30% on all the datasets. This signifies that CSDE-V-Detectors can efficiently attain highest detection rates and lowest false alarm rates for anomaly detection. Thus, the optimization of the randomly detectors of V-Detectors algorithm with CSDE is proficient and suitable for anomaly detection tasks.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Ayodele Nojeem, Lasisi
author_facet	Ayodele Nojeem, Lasisi
author_sort	Ayodele Nojeem, Lasisi
title	An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection
title_short	An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection
title_full	An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection
title_fullStr	An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection
title_full_unstemmed	An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection
title_sort	improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Sains Komputer dan Teknologi Maklumat
publishDate	2018
url	http://eprints.uthm.edu.my/202/1/24p%20LASISI%20AYODELE%20NOJEEM.pdf http://eprints.uthm.edu.my/202/2/LASISI%20AYODELE%20NOJEEM%20WATERMARK.pdf http://eprints.uthm.edu.my/202/3/LASISI%20AYODELE%20NOJEEM%20COPYRIGHT%20DECLARATION.pdf
_version_	1747830552785846272

An improved negative selection algorithm based on the hybridization of cuckoo search and differential evolution for anomaly detection

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