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     Research Journal of Applied Sciences, Engineering and Technology


Enhanced Permission Based Malware Detection in Mobile Devices Using Optimized Random Forest Classifier with PSO-GA

M. Sujithra and G. Padmavathi
Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India
Research Journal of Applied Sciences, Engineering and Technology  2016  7:732-741
http://dx.doi.org/10.19026/rjaset.12.2749  |  © The Author(s) 2016
Received: October ‎5, ‎201  |  Accepted: November ‎4, ‎2015  |  Published: April 05, 2016

Abstract

Smartphones and mobile devices are rapidly growing with their popularity as a part of global infrastructure powered communication system. As mobile devices become ubiquitous, used for a wide variety of application areas like personal communication, data storage, accessing online information, making payment, etc. The tremendous growth of smartphone usage makes it a target for malicious attackers to propagate malware attacks. Increased demand for mobile devices is due to the huge availability of applications that can be downloaded and installed easily on these devices. It is difficult for the general users to differentiate between the set of permissions which are potentially harmful and those which are not. This paper proposes to solve these issues by enhanced machine learning based malware detection framework using optimization algorithms. New classifier is developed by integrating GA and PSO with Random Forest algorithm. The Outcome from this paper is a new MSGP Malware Detection System consisting of MSGP-MDS Classifier. This reveals that classification of Android APK files using PSO plays a critical role in realizing higher accuracy with the minimum computation resource requirement.

Keywords:

Android, goodware, machine learning, malware, malware detection, optimization technique,


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Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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