Android Malware Detection: an Eigenspace Analysis Approach

Suleiman Y. Yerima; Sakir Sezer; Igor Muttik

doi:10.1109/SAI.2015.7237302

Android Malware Detection: an Eigenspace Analysis Approach

Suleiman Y. Yerima, Sakir Sezer, Igor Muttik

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

14 Citations (Scopus)

184 Downloads (Pure)

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.

Original language	English
Title of host publication	Proceedings of the 2015 Science and Information Conference
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1236-1242
Number of pages	7
DOIs	https://doi.org/10.1109/SAI.2015.7237302
Publication status	Published - 03 Sep 2015
Event	2015 SAI Conference - London, United Kingdom Duration: 28 Jul 2015 → 30 Sep 2015

Conference

Conference	2015 SAI Conference
Country	United Kingdom
City	London
Period	28/07/2015 → 30/09/2015

Keywords

malware detection
machine learning
data mining
eigenvectors
eigenvalue analysis
mobile security
Android
eigenspace
static analysis

Access to Document

10.1109/SAI.2015.7237302

Android Malware Detection: an Eigenspace Analysis Approach
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Accepted author manuscript, 940 KBLicence: Unspecified

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Cite this

Yerima, S. Y., Sezer, S., & Muttik, I. (2015). Android Malware Detection: an Eigenspace Analysis Approach. In Proceedings of the 2015 Science and Information Conference (pp. 1236-1242). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/SAI.2015.7237302

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