Open AccessA novel approach to big data analysis using deep belief network for the detection of android malware
Author(s) -
Uma Narayanan,
Varghese Paul,
Shelbi Joseph
Publication year - 2019
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v16.i3.pp1447-1454
Subject(s) - android (operating system) , malware , android malware , computer science , deep learning , computer security , static analysis , artificial intelligence , operating system , programming language
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 articial 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.