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:	2015 Science and Information Conference (SAI) pp. 1236 - 1242
Main Authors:	Yerima, Suleiman Y., Sezer, Sakir, Muttik, Igor
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.07.2015
Subjects:	Accuracy Android Androids eigenspace eigenvectors Feature extraction Humanoid robots Machine learning algorithms Malware malware detection mobile security statistical machine learning Training
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.
DOI:	10.1109/SAI.2015.7237302