A novel approach to big data analysis using deep belief network for the detection of android malware

Uma Narayanan, Varghese Paul, Shelbi Joseph


Mobile and tablets are rapidly getting the chance to be basic device in the everyday life. Android has been the most well-known versatile working structure. Regardless, inferable from the open thought of Android, amount of malware is concealed in a broad number of kind applications in Android exhibits that really undermine Android security. Deep learning is another domain of AI explore that has expanded extending thought in artificial information. In this examination, we propose to relate the features from the static examination with features from the dynamic examination of Android applications and depict malware using Deep learning systems. What's more, besides distinguishing sensitive customer data sources is fundamental for security protection in portable applications. So we propose a Novel way to deal with overseeing tremendous information examination utilizing Deep learning for the affirmation of Android malware.


An android malware, Big data, Security, Deep learning, Deep belief network

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DOI: http://doi.org/10.11591/ijeecs.v16.i3.pp1447-1454


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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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