Malware Detection in Android Using Data Mining

Suparna Dasgupta, Soumyabrata Saha and Suman Kumar Das
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Suparna Dasgupta: JIS College of Engineering, Kalyani, India
Soumyabrata Saha: JIS College of Engineering, Kalyani, India
Suman Kumar Das: JIS College of Engineering, Kalyani, India

International Journal of Natural Computing Research (IJNCR), 2017, vol. 6, issue 2, 1-17

Abstract: This article describes how as day-to-day Android users are increasing, the Internet has become the type of environment preferred by attackers to inject malicious packages. This is content with the intention of gathering critical information, spying on user details, credentials, call logs, contact details, and tracking user location. Regrettably it is very hard to detect malware even with antivirus software/packages. In addition, this type of attack is increasing day by day. In this article the authors have chosen a Supervised Learning Classification Tree-based algorithm to detect malware on the data set. Comparison amongst all the classifiers on the basis of accuracy and execution time are used to build the classifier model which has the highest executed detections.

Date: 2017
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