Improved Malware detection model with Apriori Association rule and particle swarm optimization

The incessant destruction and harmful tendency of malware on mobile devices has made malware detection an indispensable continuous field of research. Different matching/mismatching approaches have been adopted in the detection of malware which includes anomaly detection technique, misuse detection,...

Full description

Bibliographic Details
Main Authors:	Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah
Format:	Article
Language:	English English English
Published:	Hindawi Limited 2019
Subjects:	T Technology (General) TK7885 Computer engineering
Online Access:	http://irep.iium.edu.my/79657/ http://irep.iium.edu.my/79657/ http://irep.iium.edu.my/79657/ http://irep.iium.edu.my/79657/3/79657_Improved%20Malware%20Detection.pdf http://irep.iium.edu.my/79657/1/79657_Improved%20Malware%20Detection_SCOPUS.pdf http://irep.iium.edu.my/79657/2/79657_Improved%20Malware%20Detection_WOS.pdf

id	iium-79657
recordtype	eprints
spelling	iium-796572020-03-19T08:22:38Z http://irep.iium.edu.my/79657/ Improved Malware detection model with Apriori Association rule and particle swarm optimization Adebayo, Olawale Surajudeen Abdul Aziz, Normaziah T Technology (General) TK7885 Computer engineering The incessant destruction and harmful tendency of malware on mobile devices has made malware detection an indispensable continuous field of research. Different matching/mismatching approaches have been adopted in the detection of malware which includes anomaly detection technique, misuse detection, or hybrid detection technique. In order to improve the detection rate of malicious application on the Android platform, a novel knowledge-based database discovery model that improves apriori association rule mining of a priori algorithm with Particle Swarm Optimization (PSO) is proposed. Particle swarm optimization (PSO) is used to optimize the random generation of candidate detectors and parameters associated with apriori algorithm (AA) for features selection. In this method, the candidate detectors generated by particle swarm optimization form rules using apriori association rule. These rule models are used together with extraction algorithm to classify and detect malicious android application. Using a number of rule detectors, the true positive rate of detecting malicious code is maximized, while the false positive rate of wrongful detection is minimized. The results of the experiments show that the proposed a priori association rule with Particle Swarm Optimization model has remarkable improvement over the existing contemporary detection models. © 2019 Olawale Surajudeen Adebayo and Normaziah Abdul Aziz. Hindawi Limited 2019 Article PeerReviewed application/pdf en http://irep.iium.edu.my/79657/3/79657_Improved%20Malware%20Detection.pdf application/pdf en http://irep.iium.edu.my/79657/1/79657_Improved%20Malware%20Detection_SCOPUS.pdf application/pdf en http://irep.iium.edu.my/79657/2/79657_Improved%20Malware%20Detection_WOS.pdf Adebayo, Olawale Surajudeen and Abdul Aziz, Normaziah (2019) Improved Malware detection model with Apriori Association rule and particle swarm optimization. Security and Communication Networks, 2019. pp. 1-13. ISSN 1939-0114 E-ISSN 1939-0122 http://downloads.hindawi.com/journals/scn/2019/2850932.pdf 10.1155/2019/2850932
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English English
topic	T Technology (General) TK7885 Computer engineering
spellingShingle	T Technology (General) TK7885 Computer engineering Adebayo, Olawale Surajudeen Abdul Aziz, Normaziah Improved Malware detection model with Apriori Association rule and particle swarm optimization
description	The incessant destruction and harmful tendency of malware on mobile devices has made malware detection an indispensable continuous field of research. Different matching/mismatching approaches have been adopted in the detection of malware which includes anomaly detection technique, misuse detection, or hybrid detection technique. In order to improve the detection rate of malicious application on the Android platform, a novel knowledge-based database discovery model that improves apriori association rule mining of a priori algorithm with Particle Swarm Optimization (PSO) is proposed. Particle swarm optimization (PSO) is used to optimize the random generation of candidate detectors and parameters associated with apriori algorithm (AA) for features selection. In this method, the candidate detectors generated by particle swarm optimization form rules using apriori association rule. These rule models are used together with extraction algorithm to classify and detect malicious android application. Using a number of rule detectors, the true positive rate of detecting malicious code is maximized, while the false positive rate of wrongful detection is minimized. The results of the experiments show that the proposed a priori association rule with Particle Swarm Optimization model has remarkable improvement over the existing contemporary detection models. © 2019 Olawale Surajudeen Adebayo and Normaziah Abdul Aziz.
format	Article
author	Adebayo, Olawale Surajudeen Abdul Aziz, Normaziah
author_facet	Adebayo, Olawale Surajudeen Abdul Aziz, Normaziah
author_sort	Adebayo, Olawale Surajudeen
title	Improved Malware detection model with Apriori Association rule and particle swarm optimization
title_short	Improved Malware detection model with Apriori Association rule and particle swarm optimization
title_full	Improved Malware detection model with Apriori Association rule and particle swarm optimization
title_fullStr	Improved Malware detection model with Apriori Association rule and particle swarm optimization
title_full_unstemmed	Improved Malware detection model with Apriori Association rule and particle swarm optimization
title_sort	improved malware detection model with apriori association rule and particle swarm optimization
publisher	Hindawi Limited
publishDate	2019
url	http://irep.iium.edu.my/79657/ http://irep.iium.edu.my/79657/ http://irep.iium.edu.my/79657/ http://irep.iium.edu.my/79657/3/79657_Improved%20Malware%20Detection.pdf http://irep.iium.edu.my/79657/1/79657_Improved%20Malware%20Detection_SCOPUS.pdf http://irep.iium.edu.my/79657/2/79657_Improved%20Malware%20Detection_WOS.pdf
first_indexed	2023-09-18T21:51:39Z
last_indexed	2023-09-18T21:51:39Z
_version_	1777413809458118656

Improved Malware detection model with Apriori Association rule and particle swarm optimization

Similar Items