PIdroid: A novel Android malware detection system using ensemble learning methods

doi:10.1016/j.cose.2017.03.011

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PIdroid: A novel Android malware detection system using ensemble learning methods

2017-07-01

141

Fauzia Idrees *

Muttukrishnan Rajarajan

Mauro Conti

Chen, Thomas M.

Yogachandran Rahulamathavan

DOI：10.1016/j.cose.2017.03.011

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Abstract

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The extensive use of smartphones has been a major driving force behind a drastic increase of malware attacks. Covert techniques used by the malware make them hard to detect with signature based methods. In this paper, we present Plndroid a novel Permissions and Intents based framework for identifying Android malware apps. To the best of our knowledge, Plndroid is the first solution that uses a combination of permissions and intents supplemented with Ensemble methods for accurate malware detection. The proposed approach, when applied to 1,745 real world applications, provides 99.8% accuracy (which is best reported to date). Empirical results suggest that the proposed framework is effective in detection of malware apps. (C) 2017 Elsevier Ltd. All rights reserved.

Keywords:

Malware classification

Permissions

Intents

Ensemble methods

Colluding applications

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