Android malware detection: An eigenspace analysis approach

Date

2015-07

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

IEEE

Type

Conference

Peer reviewed

Yes

Abstract

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.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

Malware, mobile security, eigenvectors, statistical machine learning, data security, feature selection, android malware detection

Citation

Yerima, S. Y., Sezer, S., Muttik, I. (2015) Android Malware Detection: An Eigenspace Analysis Approach. In: Proceedings of the 2015 Science and Information Conference (SAI), London, UK, 28-30 July 2015, pp. 1236-1242

Rights

Research Institute

Cyber Technology Institute (CTI)