Popular tools for malware data analysis

With the arising of smartphone usage, especially for Android OS, users are relying on their mobile devices increasingly. However, Android Malware brings significant threats to the eco-system. In this project, several effective Malware detection tools are implemented and afterwards evaluated on their...

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Main Author: Liu, Jinliang
Other Authors: Chen Lihui
Format: Theses and Dissertations
Language:English
Published: 2016
Subjects:
Online Access:http://hdl.handle.net/10356/68976
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-689762023-07-04T15:05:13Z Popular tools for malware data analysis Liu, Jinliang Chen Lihui School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing With the arising of smartphone usage, especially for Android OS, users are relying on their mobile devices increasingly. However, Android Malware brings significant threats to the eco-system. In this project, several effective Malware detection tools are implemented and afterwards evaluated on their accuracy and efficiency. Also, several commonly used classifiers are implemented and their performances are compared in classifying Android Malware. Additionally, concept drift in Android Malware is studied and evaluated on certain Malware datasets. Master of Science (Signal Processing) 2016-08-22T02:11:04Z 2016-08-22T02:11:04Z 2016 Thesis http://hdl.handle.net/10356/68976 en 84 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::Electrical and electronic engineering::Electronic systems::Signal processing
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Liu, Jinliang
Popular tools for malware data analysis
description With the arising of smartphone usage, especially for Android OS, users are relying on their mobile devices increasingly. However, Android Malware brings significant threats to the eco-system. In this project, several effective Malware detection tools are implemented and afterwards evaluated on their accuracy and efficiency. Also, several commonly used classifiers are implemented and their performances are compared in classifying Android Malware. Additionally, concept drift in Android Malware is studied and evaluated on certain Malware datasets.
author2 Chen Lihui
author_facet Chen Lihui
Liu, Jinliang
format Theses and Dissertations
author Liu, Jinliang
author_sort Liu, Jinliang
title Popular tools for malware data analysis
title_short Popular tools for malware data analysis
title_full Popular tools for malware data analysis
title_fullStr Popular tools for malware data analysis
title_full_unstemmed Popular tools for malware data analysis
title_sort popular tools for malware data analysis
publishDate 2016
url http://hdl.handle.net/10356/68976
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