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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/24695

Title: PIndroid: A novel Android malware detection system using ensemble learning
Authors: Idrees, Fauzia
Rajarajan, Muttukrishnan
Conti, Mauro
Rahulamathavan, Yogachandran
Chen, Tom
Keywords: Malware classification
Permissions
Intents
Ensemble methods
Colluding applications
Issue Date: 2017
Publisher: © Elsevier
Citation: IDREES, F. ...et al., 2017. PIndroid: A novel Android malware detection system using ensemble learning. Computers and Security, 68, pp. 36–46.
Abstract: The extensive usage of smartphones has been the major driving force behind a drastic increase of new security threats. The stealthy techniques used by malware make them hard to detect with signature based intrusion detection and anti-malware methods. In this paper, we present PIndroid|a novel Permissions and Intents based framework for identifying Android malware apps. To the best of our knowledge, PIndroid is the first solution that uses a combination of permissions and intents supplemented with multiple stages of classifiers for malware detection. Ensemble techniques are applied for optimization of detection results. We apply the proposed approach on 1,745 real world applications and obtain 99.8% accuracy which is the best reported to date. Empirical results suggest that our proposed framework built on permissions and intents is effective in detecting malware applications.
Description: This paper is in closed access until 31st Mar 2018.
Version: Accepted for publication
DOI: 10.1016/j.cose.2017.03.011
URI: https://dspace.lboro.ac.uk/2134/24695
Publisher Link: http://doi.org/10.1016/j.cose.2017.03.011
ISSN: 1872-6208
Appears in Collections:Closed Access (Loughborough University London)

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