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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,...

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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/79657/3/79657_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
http://irep.iium.edu.my/79657/
http://downloads.hindawi.com/journals/scn/2019/2850932.pdf
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spelling my.iium.irep.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
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
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/79657/3/79657_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
http://irep.iium.edu.my/79657/
http://downloads.hindawi.com/journals/scn/2019/2850932.pdf
_version_ 1662753891838590976
score 13.211869

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