Bio-Inspired Computational Paradigm for Feature Investigation and Malware Detection: Interactive Analytics

Recently, people rely on mobile devices to conduct their daily fundamental activities. Simultaneously, most of the people prefer devices with Android operating system. As the demand expands, deceitful authors develop malware to compromise Android for private and money purposes. Consequently, securit...

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Main Authors:	Ahmad, Firdaus, Nor Badrul, Anuar, Mohd Faizal, Ab Razak, Sangaiah, Arun Kumar
Format:	Article
Language:	English English
Published:	Springer US 2017
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/17481/ http://umpir.ump.edu.my/id/eprint/17481/ http://umpir.ump.edu.my/id/eprint/17481/ http://umpir.ump.edu.my/id/eprint/17481/1/Bio-inspired%20computational%20paradigm%20for%20feature%20investigation%20and%20malware%20detection-%20interactive%20analytics.pdf http://umpir.ump.edu.my/id/eprint/17481/12/Bio-inspired%20computational%20paradigm%20for%20feature%20investigation%20and%20malware%20detection-%20interactive%20analytics%201.pdf

id	ump-17481
recordtype	eprints
spelling	ump-174812017-07-21T01:35:34Z http://umpir.ump.edu.my/id/eprint/17481/ Bio-Inspired Computational Paradigm for Feature Investigation and Malware Detection: Interactive Analytics Ahmad, Firdaus Nor Badrul, Anuar Mohd Faizal, Ab Razak Sangaiah, Arun Kumar QA76 Computer software Recently, people rely on mobile devices to conduct their daily fundamental activities. Simultaneously, most of the people prefer devices with Android operating system. As the demand expands, deceitful authors develop malware to compromise Android for private and money purposes. Consequently, security analysts have to conduct static and dynamic analyses to counter malware violation. In this paper, we adopt static analysis which only requests minimal resource consumption and rapid processing. However, finding a minimum set of features in the static analysis are vital because it removes irrelevant data, reduces the runtime of machine learning detection and reduces the dimensionality of datasets. Therefore, in this paper, we investigate three categories of features, which are permissions, directory path, and telephony. This investigation considers the features frequency as well as repeatedly used in each application. Subsequently, this study evaluates the proposed features in three bio-inspired machine learning classifiers in artificial neural network (ANN) category to signify the usefulness of ANN type in uncovering unknown malware. The classifiers are multilayer perceptron (MLP), voted perceptron (VP) and radial basis function network (RBFN). Among all these three classifiers, the outstanding outcomes acquire is the MLP, which achieves 90% in accuracy and 87% in true positive rate (TPR), as well as 97% accuracy in our Bio Analyzer prediction system. Springer US 2017 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/17481/1/Bio-inspired%20computational%20paradigm%20for%20feature%20investigation%20and%20malware%20detection-%20interactive%20analytics.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/17481/12/Bio-inspired%20computational%20paradigm%20for%20feature%20investigation%20and%20malware%20detection-%20interactive%20analytics%201.pdf Ahmad, Firdaus and Nor Badrul, Anuar and Mohd Faizal, Ab Razak and Sangaiah, Arun Kumar (2017) Bio-Inspired Computational Paradigm for Feature Investigation and Malware Detection: Interactive Analytics. Multimedia Tools and Applications. pp. 1-37. ISSN 1380-7501(print); 1573-7721(online) https://doi.org/10.1007/s11042-017-4586-0 DOI: 10.1007/s11042-017-4586-0
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Ahmad, Firdaus Nor Badrul, Anuar Mohd Faizal, Ab Razak Sangaiah, Arun Kumar Bio-Inspired Computational Paradigm for Feature Investigation and Malware Detection: Interactive Analytics
description	Recently, people rely on mobile devices to conduct their daily fundamental activities. Simultaneously, most of the people prefer devices with Android operating system. As the demand expands, deceitful authors develop malware to compromise Android for private and money purposes. Consequently, security analysts have to conduct static and dynamic analyses to counter malware violation. In this paper, we adopt static analysis which only requests minimal resource consumption and rapid processing. However, finding a minimum set of features in the static analysis are vital because it removes irrelevant data, reduces the runtime of machine learning detection and reduces the dimensionality of datasets. Therefore, in this paper, we investigate three categories of features, which are permissions, directory path, and telephony. This investigation considers the features frequency as well as repeatedly used in each application. Subsequently, this study evaluates the proposed features in three bio-inspired machine learning classifiers in artificial neural network (ANN) category to signify the usefulness of ANN type in uncovering unknown malware. The classifiers are multilayer perceptron (MLP), voted perceptron (VP) and radial basis function network (RBFN). Among all these three classifiers, the outstanding outcomes acquire is the MLP, which achieves 90% in accuracy and 87% in true positive rate (TPR), as well as 97% accuracy in our Bio Analyzer prediction system.
format	Article
author	Ahmad, Firdaus Nor Badrul, Anuar Mohd Faizal, Ab Razak Sangaiah, Arun Kumar
author_facet	Ahmad, Firdaus Nor Badrul, Anuar Mohd Faizal, Ab Razak Sangaiah, Arun Kumar
author_sort	Ahmad, Firdaus
title	Bio-Inspired Computational Paradigm for Feature Investigation and Malware Detection: Interactive Analytics
title_short	Bio-Inspired Computational Paradigm for Feature Investigation and Malware Detection: Interactive Analytics
title_full	Bio-Inspired Computational Paradigm for Feature Investigation and Malware Detection: Interactive Analytics
title_fullStr	Bio-Inspired Computational Paradigm for Feature Investigation and Malware Detection: Interactive Analytics
title_full_unstemmed	Bio-Inspired Computational Paradigm for Feature Investigation and Malware Detection: Interactive Analytics
title_sort	bio-inspired computational paradigm for feature investigation and malware detection: interactive analytics
publisher	Springer US
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/17481/ http://umpir.ump.edu.my/id/eprint/17481/ http://umpir.ump.edu.my/id/eprint/17481/ http://umpir.ump.edu.my/id/eprint/17481/1/Bio-inspired%20computational%20paradigm%20for%20feature%20investigation%20and%20malware%20detection-%20interactive%20analytics.pdf http://umpir.ump.edu.my/id/eprint/17481/12/Bio-inspired%20computational%20paradigm%20for%20feature%20investigation%20and%20malware%20detection-%20interactive%20analytics%201.pdf
first_indexed	2023-09-18T22:24:10Z
last_indexed	2023-09-18T22:24:10Z
_version_	1777415854737063936

Bio-Inspired Computational Paradigm for Feature Investigation and Malware Detection: Interactive Analytics

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