Android malware detection using big data analytics techniques

Application clones have been found in many third party markets, which not only make damage to the interest of original application developers, but also pose threats to security and privacy of mobile users. This project implements 3DCFG, which is a centroid-based clone detection method. Experiments o...

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Bibliographic Details
Main Author:	Han, Bihui
Other Authors:	Chen Lihui
Format:	Final Year Project
Language:	English
Published:	2016
Subjects:	DRNTU::Engineering
Online Access:	http://hdl.handle.net/10356/67775
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-67775
record_format	dspace
spelling	sg-ntu-dr.10356-677752023-07-07T15:42:14Z Android malware detection using big data analytics techniques Han, Bihui Chen Lihui School of Electrical and Electronic Engineering DRNTU::Engineering Application clones have been found in many third party markets, which not only make damage to the interest of original application developers, but also pose threats to security and privacy of mobile users. This project implements 3DCFG, which is a centroid-based clone detection method. Experiments on 10,000 Android applications demonstrate the effectiveness and scalability of the 3DCFG method. To further improve the detection speed and accuracy, a combination usage of three library detection tools is used to filter third party libraries before clone detection. A clustering-based library detection method proposed from WuKong is implemented and it is used as a library detection tool in 3DCFG library filtering part. Bachelor of Engineering 2016-05-20T06:14:57Z 2016-05-20T06:14:57Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/67775 en Nanyang Technological University 53 p. application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	DRNTU::Engineering
spellingShingle	DRNTU::Engineering Han, Bihui Android malware detection using big data analytics techniques
description	Application clones have been found in many third party markets, which not only make damage to the interest of original application developers, but also pose threats to security and privacy of mobile users. This project implements 3DCFG, which is a centroid-based clone detection method. Experiments on 10,000 Android applications demonstrate the effectiveness and scalability of the 3DCFG method. To further improve the detection speed and accuracy, a combination usage of three library detection tools is used to filter third party libraries before clone detection. A clustering-based library detection method proposed from WuKong is implemented and it is used as a library detection tool in 3DCFG library filtering part.
author2	Chen Lihui
author_facet	Chen Lihui Han, Bihui
format	Final Year Project
author	Han, Bihui
author_sort	Han, Bihui
title	Android malware detection using big data analytics techniques
title_short	Android malware detection using big data analytics techniques
title_full	Android malware detection using big data analytics techniques
title_fullStr	Android malware detection using big data analytics techniques
title_full_unstemmed	Android malware detection using big data analytics techniques
title_sort	android malware detection using big data analytics techniques
publishDate	2016
url	http://hdl.handle.net/10356/67775
_version_	1772827218779570176

Android malware detection using big data analytics techniques

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