PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

 

e-ISSN 2231-8542
ISSN 1511-3701

Home / Regular Issue / JTAS Vol. 25 (S) Jun. 2017 / JST-S0384-2017

 

Android Malware Detection using Deep Belief Network

Wael Farouk Elsersy and Nor Badrul Anuar

Pertanika Journal of Tropical Agricultural Science, Volume 25, Issue S, June 2017

Keywords: Android malware detection, Deep belief network, Feature learning, Machine learning algorithms

Published on: 12 Mac 2018

Over the last few years, the Android smartphone had faced attacks from malware and malware variants, as there is no effective commercial Android security framework in the market. Thus, using machine learning algorithms to detect Android malware applications that can fit with the smartphone resources limitations became popular. This paper used state of the art Deep Belief Network in Android malware detection. The Lasso is one of the best interpretable â„“1-regularisation techniques which proved to be an efficient feature selection embedded in learning algorithm. The selected features subset of Restricted Boltzmann Machines tuned by Harmony Search feature reduction with Deep Belief Network classifier was used, achieving 85.22% Android malware detection accuracy.

ISSN 1511-3701

e-ISSN 2231-8542

Article ID

JST-S0384-2017

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