Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization

Several machine learning techniques based on supervised learning have been adopted in the classification of malware. However, only supervised learning techniques have proofed insufficient for malware classification task. This paper presents a classification of android malware using candidate detecto...

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Main Authors:	Adebayo, Olawale Surajudeen, Abdul Aziz, Normaziah
Format:	Conference or Workshop Item
Language:	English
Published:	IEEE 2014
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://irep.iium.edu.my/48604/ http://irep.iium.edu.my/48604/ http://irep.iium.edu.my/48604/1/Android_Malware_Classification_-_Static_Code_anaysis.pdf

id	iium-48604
recordtype	eprints
spelling	iium-486042016-06-05T18:55:15Z http://irep.iium.edu.my/48604/ Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization Adebayo, Olawale Surajudeen Abdul Aziz, Normaziah QA75 Electronic computers. Computer science Several machine learning techniques based on supervised learning have been adopted in the classification of malware. However, only supervised learning techniques have proofed insufficient for malware classification task. This paper presents a classification of android malware using candidate detectors generated from an unsupervised association rule of Apriori algorithm improved with particle swarm optimization to train three different supervised classifiers. In this method, features were extracted from Android applications byte-code through static code analysis, selected and were used to train supervised classifiers. Using a number of candidate 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 combined technique has remarkable benefits over the detection using only supervised or unsupervised learners. IEEE 2014-04-01 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/48604/1/Android_Malware_Classification_-_Static_Code_anaysis.pdf Adebayo, Olawale Surajudeen and Abdul Aziz, Normaziah (2014) Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization. In: 2014 World Congress on Information and Communication Technologies (WICT 2014), 8-10 December 2014, Melaka, Malaysia. http://www.mirlabs.net/wict14/proceedings/html/paper78.xml
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Adebayo, Olawale Surajudeen Abdul Aziz, Normaziah Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization
description	Several machine learning techniques based on supervised learning have been adopted in the classification of malware. However, only supervised learning techniques have proofed insufficient for malware classification task. This paper presents a classification of android malware using candidate detectors generated from an unsupervised association rule of Apriori algorithm improved with particle swarm optimization to train three different supervised classifiers. In this method, features were extracted from Android applications byte-code through static code analysis, selected and were used to train supervised classifiers. Using a number of candidate 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 combined technique has remarkable benefits over the detection using only supervised or unsupervised learners.
format	Conference or Workshop Item
author	Adebayo, Olawale Surajudeen Abdul Aziz, Normaziah
author_facet	Adebayo, Olawale Surajudeen Abdul Aziz, Normaziah
author_sort	Adebayo, Olawale Surajudeen
title	Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization
title_short	Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization
title_full	Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization
title_fullStr	Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization
title_full_unstemmed	Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization
title_sort	android malware classification using static code analysis and apriori algorithm improved with particle swarm optimization
publisher	IEEE
publishDate	2014
url	http://irep.iium.edu.my/48604/ http://irep.iium.edu.my/48604/ http://irep.iium.edu.my/48604/1/Android_Malware_Classification_-_Static_Code_anaysis.pdf
first_indexed	2023-09-18T21:08:54Z
last_indexed	2023-09-18T21:08:54Z
_version_	1777411120150085632

Android Malware classification using static code analysis and Apriori algorithm improved with particle swarm optimization

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