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Malware Detection in Android Using Data Mining

Malware Detection in Android Using Data Mining

Suparna Dasgupta, Soumyabrata Saha, Suman Kumar Das
Copyright: © 2017 |Volume: 6 |Issue: 2 |Pages: 17
ISSN: 1947-928X|EISSN: 1947-9298|EISBN13: 9781522513506|DOI: 10.4018/IJNCR.2017070101
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MLA

Dasgupta, Suparna, et al. "Malware Detection in Android Using Data Mining." IJNCR vol.6, no.2 2017: pp.1-17. http://doi.org/10.4018/IJNCR.2017070101

APA

Dasgupta, S., Saha, S., & Das, S. K. (2017). Malware Detection in Android Using Data Mining. International Journal of Natural Computing Research (IJNCR), 6(2), 1-17. http://doi.org/10.4018/IJNCR.2017070101

Chicago

Dasgupta, Suparna, Soumyabrata Saha, and Suman Kumar Das. "Malware Detection in Android Using Data Mining," International Journal of Natural Computing Research (IJNCR) 6, no.2: 1-17. http://doi.org/10.4018/IJNCR.2017070101

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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.

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