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Power Consumption-based Application Classification and Malware Detection on Android Using Machine-Learning Techniques

Authors:
Thomas Zefferer
Peter Teufl
David Derler
Klaus Potzmader
Alexander Oprisnik
Hubert Gasparitz
Andrea Hoeller

Keywords: Android; power consumption; application classification; malware detection; machine learning}

Abstract:
Mobile computing has significantly gained importance during the past years and is expected to remain one of the most relevant future computing trends. Smartphones represent a key component of mobile computing based solutions and allow end users to conveniently access services and information. Due to their continuously growing importance and popularity, smartphones have recently become a common target for malware. Unfortunately, capabilities of malware-detection applications on smartphones are limited, as implemented security features such as sandboxing or fine-grained permission models restrict capabilities of third-party applications. These restrictions prevent malware-detection applications from accessing information, which is required to identify malware, and hence render the implementation of reliable malware-detection solutions on smartphones difficult. To overcome this issue, we propose an alternative malware-detection method for smartphones that relies on the smartphone's measured power consumption. We propose two different machine-learning techniques that allow for a classification of applications according to their power consumption and hence facilitate the identification of suspicious and potentially malicious software components. The capabilities of the proposed techniques have been assessed by means of an evaluation with real-world applications running on physical smartphones. The results of this evaluation process demonstrate the applicability of power consumption based classification and malware-detection approaches in general and of the two proposed machine-learning techniques in particular.

Pages: 26 to 31

Copyright: Copyright (c) IARIA, 2013

Publication date: May 27, 2013

Published in: conference

ISSN: 2308-3735

ISBN: 978-1-61208-272-1

Location: Valencia, Spain

Dates: from May 27, 2013 to June 1, 2013