A static analysis approach for Android permission-based malware detection systems.

The evolution of malware is causing mobile devices to crash with increasing frequency. Therefore, adequate security evaluations that detect Android malware are crucial. Two techniques can be used in this regard: Static analysis, which meticulously examines the full codes of applications, and dynamic...

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Autores principales:	Juliza Mohamad Arif, Mohd Faizal Ab Razak, Suryanti Awang, Sharfah Ratibah Tuan Mat, Nor Syahidatul Nadiah Ismail, Ahmad Firdaus
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/351777d67fd942f180495755f102eae9
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id	oai:doaj.org-article:351777d67fd942f180495755f102eae9
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spelling	oai:doaj.org-article:351777d67fd942f180495755f102eae92021-12-02T20:13:53ZA static analysis approach for Android permission-based malware detection systems.1932-620310.1371/journal.pone.0257968https://doaj.org/article/351777d67fd942f180495755f102eae92021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257968https://doaj.org/toc/1932-6203The evolution of malware is causing mobile devices to crash with increasing frequency. Therefore, adequate security evaluations that detect Android malware are crucial. Two techniques can be used in this regard: Static analysis, which meticulously examines the full codes of applications, and dynamic analysis, which monitors malware behaviour. While both perform security evaluations successfully, there is still room for improvement. The goal of this research is to examine the effectiveness of static analysis to detect Android malware by using permission-based features. This study proposes machine learning with different sets of classifiers was used to evaluate Android malware detection. The feature selection method in this study was applied to determine which features were most capable of distinguishing malware. A total of 5,000 Drebin malware samples and 5,000 Androzoo benign samples were utilised. The performances of the different sets of classifiers were then compared. The results indicated that with a TPR value of 91.6%, the Random Forest algorithm achieved the highest level of accuracy in malware detection.Juliza Mohamad ArifMohd Faizal Ab RazakSuryanti AwangSharfah Ratibah Tuan MatNor Syahidatul Nadiah IsmailAhmad FirdausPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0257968 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Juliza Mohamad Arif Mohd Faizal Ab Razak Suryanti Awang Sharfah Ratibah Tuan Mat Nor Syahidatul Nadiah Ismail Ahmad Firdaus A static analysis approach for Android permission-based malware detection systems.
description	The evolution of malware is causing mobile devices to crash with increasing frequency. Therefore, adequate security evaluations that detect Android malware are crucial. Two techniques can be used in this regard: Static analysis, which meticulously examines the full codes of applications, and dynamic analysis, which monitors malware behaviour. While both perform security evaluations successfully, there is still room for improvement. The goal of this research is to examine the effectiveness of static analysis to detect Android malware by using permission-based features. This study proposes machine learning with different sets of classifiers was used to evaluate Android malware detection. The feature selection method in this study was applied to determine which features were most capable of distinguishing malware. A total of 5,000 Drebin malware samples and 5,000 Androzoo benign samples were utilised. The performances of the different sets of classifiers were then compared. The results indicated that with a TPR value of 91.6%, the Random Forest algorithm achieved the highest level of accuracy in malware detection.
format	article
author	Juliza Mohamad Arif Mohd Faizal Ab Razak Suryanti Awang Sharfah Ratibah Tuan Mat Nor Syahidatul Nadiah Ismail Ahmad Firdaus
author_facet	Juliza Mohamad Arif Mohd Faizal Ab Razak Suryanti Awang Sharfah Ratibah Tuan Mat Nor Syahidatul Nadiah Ismail Ahmad Firdaus
author_sort	Juliza Mohamad Arif
title	A static analysis approach for Android permission-based malware detection systems.
title_short	A static analysis approach for Android permission-based malware detection systems.
title_full	A static analysis approach for Android permission-based malware detection systems.
title_fullStr	A static analysis approach for Android permission-based malware detection systems.
title_full_unstemmed	A static analysis approach for Android permission-based malware detection systems.
title_sort	static analysis approach for android permission-based malware detection systems.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/351777d67fd942f180495755f102eae9
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A static analysis approach for Android permission-based malware detection systems.

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