High Accuracy Android Malware Detection Using Ensemble Learning

Date

2015-10-12

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

The Institution of Engineering and Technology

Type

Article

Peer reviewed

Yes

Abstract

With over 50 billion downloads and more than 1.3 million apps in Google's official market, Android has continued to gain popularity among smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature-based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus, this study proposes an approach that utilises ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3–99% detection accuracy with very low false positive rates.

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

Android Malware, Malware Detection, Ensemble Learning, Data Security, Machine Learning, Smart phone security

Citation

Yerima, S. Y., Sezer, S., Muttik, I. (2015) High accuracy android malware detection using ensemble learning. IET Information Security, 9(6), pp. 313-320.

Rights

Research Institute

Cyber Technology Institute (CTI)