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Analytics on malicious android applications

The widespread of mobile applications has led to increase smartphone malware. Detecting malware requires extracting features to determine the malware apps from non-malware apps. To understand malware apps' features, we need a better understanding of the requested permissions in manifest file of...

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Main Authors:	Abubaker, H., Shamsuddin, Siti Mariyam, Ali, Aida
Format:	Article
Language:	English
Published:	International Center for Scientific Research and Studies 2018
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.utm.my/id/eprint/85807/1/SitiMariyamShamsuddin2018_AnalyticsonMaliciousAndroidApplications.pdf http://eprints.utm.my/id/eprint/85807/
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id	my.utm.85807
record_format	eprints
spelling	my.utm.858072020-07-28T02:45:36Z http://eprints.utm.my/id/eprint/85807/ Analytics on malicious android applications Abubaker, H. Shamsuddin, Siti Mariyam Ali, Aida QA75 Electronic computers. Computer science The widespread of mobile applications has led to increase smartphone malware. Detecting malware requires extracting features to determine the malware apps from non-malware apps. To understand malware apps' features, we need a better understanding of the requested permissions in manifest file of apk file. In this paper, we present our framework based on extracting apk's permissions with the aims to detect the malware upon granted permissions in mobile app. The permissions keywords are extracted from the manifest file of apk file using VirusTotal website. These collected applications and their permissions keywords will go through pre-data analytics process before being trained to various machine learning classifiers. We collected around 30 apps from Google play as non-malware apps and 30 malicious apps from different sources such as PROGuard, Contagio Mobile blog and the Drebin dataset. The permissions keywords of the collected apk are extracted and saved to build final dataset that contains 50 samples of benign and malignant applications with the final collections of permissions keywords. Finally, the dataset is fed to machine learning. By utilizing several classifiers such as NaiveBayes, sequential minimal optimization (SMO), Decision Table, ZeroR and Decision trees (J48 and Random Forests, the results show that sequential minimal optimization (SMO) classifier achieved high performance in the detection rate of the classifier with an acceptable accuracy of 76 %. International Center for Scientific Research and Studies 2018-03 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/85807/1/SitiMariyamShamsuddin2018_AnalyticsonMaliciousAndroidApplications.pdf Abubaker, H. and Shamsuddin, Siti Mariyam and Ali, Aida (2018) Analytics on malicious android applications. International Journal of Advances in Soft Computing and its Applications, 10 (1). pp. 106-118. ISSN 2074-8523
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Abubaker, H. Shamsuddin, Siti Mariyam Ali, Aida Analytics on malicious android applications
description	The widespread of mobile applications has led to increase smartphone malware. Detecting malware requires extracting features to determine the malware apps from non-malware apps. To understand malware apps' features, we need a better understanding of the requested permissions in manifest file of apk file. In this paper, we present our framework based on extracting apk's permissions with the aims to detect the malware upon granted permissions in mobile app. The permissions keywords are extracted from the manifest file of apk file using VirusTotal website. These collected applications and their permissions keywords will go through pre-data analytics process before being trained to various machine learning classifiers. We collected around 30 apps from Google play as non-malware apps and 30 malicious apps from different sources such as PROGuard, Contagio Mobile blog and the Drebin dataset. The permissions keywords of the collected apk are extracted and saved to build final dataset that contains 50 samples of benign and malignant applications with the final collections of permissions keywords. Finally, the dataset is fed to machine learning. By utilizing several classifiers such as NaiveBayes, sequential minimal optimization (SMO), Decision Table, ZeroR and Decision trees (J48 and Random Forests, the results show that sequential minimal optimization (SMO) classifier achieved high performance in the detection rate of the classifier with an acceptable accuracy of 76 %.
format	Article
author	Abubaker, H. Shamsuddin, Siti Mariyam Ali, Aida
author_facet	Abubaker, H. Shamsuddin, Siti Mariyam Ali, Aida
author_sort	Abubaker, H.
title	Analytics on malicious android applications
title_short	Analytics on malicious android applications
title_full	Analytics on malicious android applications
title_fullStr	Analytics on malicious android applications
title_full_unstemmed	Analytics on malicious android applications
title_sort	analytics on malicious android applications
publisher	International Center for Scientific Research and Studies
publishDate	2018
url	http://eprints.utm.my/id/eprint/85807/1/SitiMariyamShamsuddin2018_AnalyticsonMaliciousAndroidApplications.pdf http://eprints.utm.my/id/eprint/85807/
_version_	1674066210858205184
score	13.211869

Analytics on malicious android applications

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