Machine learning methods for Android malware detection

With the Android mobile device becoming increasingly popular, the Android application market has become a main target of the malware attacks. Therefore, many methods have been used to protect the mobile application users from being attacked. However, those methods have shortcomings in detecting the...

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Main Author: Xu, Zhengzi
Other Authors: Liu Yang
Format: Final Year Project
Language:English
Published: 2015
Subjects:
Online Access:http://hdl.handle.net/10356/62566
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-625662023-03-03T20:43:19Z Machine learning methods for Android malware detection Xu, Zhengzi Liu Yang School of Computer Engineering DRNTU::Engineering::Computer science and engineering With the Android mobile device becoming increasingly popular, the Android application market has become a main target of the malware attacks. Therefore, many methods have been used to protect the mobile application users from being attacked. However, those methods have shortcomings in detecting the malware within a short time, and can be easily bypassed. To detect the malware before the installed time, and overcome the drawbacks of dynamic analysis and signature based analysis, the machine learning based malware detection methods has been proposed. In this project, I have adopted this approach to develop a tool to extract Android application features, and built the classification model using the generated feature sets. The result shows that classification the model can reach 98% accuracy in predicting the maliciousness of the application. I have also generated the transformation attack samples, which will be used in further machine learning based malware detection studies. Bachelor of Engineering (Computer Science) 2015-04-20T08:35:46Z 2015-04-20T08:35:46Z 2015 2015 Final Year Project (FYP) http://hdl.handle.net/10356/62566 en Nanyang Technological University 41 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::Computer science and engineering
spellingShingle DRNTU::Engineering::Computer science and engineering
Xu, Zhengzi
Machine learning methods for Android malware detection
description With the Android mobile device becoming increasingly popular, the Android application market has become a main target of the malware attacks. Therefore, many methods have been used to protect the mobile application users from being attacked. However, those methods have shortcomings in detecting the malware within a short time, and can be easily bypassed. To detect the malware before the installed time, and overcome the drawbacks of dynamic analysis and signature based analysis, the machine learning based malware detection methods has been proposed. In this project, I have adopted this approach to develop a tool to extract Android application features, and built the classification model using the generated feature sets. The result shows that classification the model can reach 98% accuracy in predicting the maliciousness of the application. I have also generated the transformation attack samples, which will be used in further machine learning based malware detection studies.
author2 Liu Yang
author_facet Liu Yang
Xu, Zhengzi
format Final Year Project
author Xu, Zhengzi
author_sort Xu, Zhengzi
title Machine learning methods for Android malware detection
title_short Machine learning methods for Android malware detection
title_full Machine learning methods for Android malware detection
title_fullStr Machine learning methods for Android malware detection
title_full_unstemmed Machine learning methods for Android malware detection
title_sort machine learning methods for android malware detection
publishDate 2015
url http://hdl.handle.net/10356/62566
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