Android Malware Detection: an Eigenspace Analysis Approach

The battle to mitigate Android malware has become more critical with the emergence of new strains incorporating increasingly sophisticated evasion techniques, in turn necessitating more advanced detection capabilities. Hence, in this paper we propose and evaluate a machine learning based approach ba...

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Bibliographic Details
Published in:	arXiv.org
Main Authors:	Yerima, Suleiman Y, Sezer, Sakir, Muttik, Igor
Format:	Paper
Language:	English
Published:	Ithaca Cornell University Library, arXiv.org 27.07.2016
Subjects:	Empirical analysis Machine learning Malware
ISSN:	2331-8422
Online Access:	Get full text
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Summary:	The battle to mitigate Android malware has become more critical with the emergence of new strains incorporating increasingly sophisticated evasion techniques, in turn necessitating more advanced detection capabilities. Hence, in this paper we propose and evaluate a machine learning based approach based on eigenspace analysis for Android malware detection using features derived from static analysis characterization of Android applications. Empirical evaluation with a dataset of real malware and benign samples show that detection rate of over 96% with a very low false positive rate is achievable using the proposed method.
Bibliography:	SourceType-Working Papers-1 ObjectType-Working Paper/Pre-Print-1 content type line 50
ISSN:	2331-8422
DOI:	10.48550/arxiv.1607.08087