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Analysis of Bayesian classification-based approaches for Android malware detection

Content Provider	IET Digital Library
Author	Yerima, Suleiman Y. Sezer, Sakir Williams, Gavin Mc
Abstract	Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, the authors develop and analyse proactive machine-learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification-based solutions for detecting unknown Android malware.
Starting Page	25
Ending Page	36
Page Count	12
ISSN	17518709
Volume Number	8
e-ISSN	17518717
Issue Number	Issue 1, Jan (2014)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ifs/8/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ifs.2013.0095
Journal	IET Information Security
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Android Malware Detection Data Security Invasive Software Knowledge Engineering Technique Learning in AI Machine Learning Mobile Malware Mobile Radio System Operating System Operating System Kernels Pattern Classification Pervasive Computing Signature Based Scanning Smart Phones Smartphone Static Analysis Static Analytic Bayesian Classification Ubiquitous
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Information Systems Software

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