Please use this identifier to cite or link to this item:
https://ptsldigital.ukm.my/jspui/handle/123456789/578548| Title: | Android malware detection using deep belief network |
| Authors: | Wael Farouk Elsersy (UM) Nor Badrul Anuar (UM) |
| Keywords: | Android malware detection Deep belief network Feature learning Machine learning algorithms |
| Issue Date: | Jun-2017 |
| Description: | 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 ?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. |
| News Source: | Pertanika Journals |
| ISSN: | 0128-7680 |
| Volume: | 25 |
| Pages: | 143-150 |
| Publisher: | Universiti Putra Malaysia Press |
| Appears in Collections: | Journal Content Pages/ Kandungan Halaman Jurnal |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_116013+Source01+Source010.PDF | 830.32 kB | Adobe PDF |  View/Open |
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