Malware detection for mobile devices
Mobile devices have increasingly become targets for malware due to the lucrative rewards for user's personal information. As such, malware detection and classification tools are valuable and sought after by many agencies. This report aims to discuss an automated system for extracting learning f...
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2017
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sg-ntu-dr.10356-702082023-03-03T20:23:16Z Malware detection for mobile devices Chia, Jia Hong Lin Shang Wei School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Document and text processing Mobile devices have increasingly become targets for malware due to the lucrative rewards for user's personal information. As such, malware detection and classification tools are valuable and sought after by many agencies. This report aims to discuss an automated system for extracting learning features to detect and classify malware that uses the native library to target the Android operating system. The report will also present a parser to extract the sequence of API calls to depict the behavior of a given application. Through the utilization of a reverse engineering tool and a disassembler, the extracted features can be used to classify malware with the use of Machine Learning Technique Bachelor of Engineering (Computer Science) 2017-04-17T01:15:34Z 2017-04-17T01:15:34Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/70208 en Nanyang Technological University 62 p. application/pdf |
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DRNTU::Engineering::Computer science and engineering::Computing methodologies::Document and text processing Chia, Jia Hong Malware detection for mobile devices |
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Mobile devices have increasingly become targets for malware due to the lucrative rewards for user's personal information. As such, malware detection and classification tools are valuable and sought after by many agencies. This report aims to discuss an automated system for extracting learning features to detect and classify malware that uses the native library to target the Android operating system. The report will also present a parser to extract the sequence of API calls to depict the behavior of a given application. Through the utilization of a reverse engineering tool and a disassembler, the extracted features can be used to classify malware with the use of Machine Learning Technique |
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Lin Shang Wei |
author_facet |
Lin Shang Wei Chia, Jia Hong |
format |
Final Year Project |
author |
Chia, Jia Hong |
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Chia, Jia Hong |
title |
Malware detection for mobile devices |
title_short |
Malware detection for mobile devices |
title_full |
Malware detection for mobile devices |
title_fullStr |
Malware detection for mobile devices |
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Malware detection for mobile devices |
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malware detection for mobile devices |
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2017 |
url |
http://hdl.handle.net/10356/70208 |
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1759857776884973568 |