Machine Learning Detection and Analysis on Obfuscated Android Malware

Zhang, Yanxin

Machine Learning Detection and Analysis on Obfuscated Android Malware

Zhang, Yanxin

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Publication Type:: Thesis
Issue Date:: 2022

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Field	Value	Language
dc.contributor.author	Zhang, Yanxin
dc.date.accessioned	2022-07-11T22:56:17Z
dc.date.available	2022-07-11T22:56:17Z
dc.date.issued	2022
dc.identifier.uri	http://hdl.handle.net/10453/158775
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	Android is the leading mobile operating system, accounting for more than 80% of the market share. Millions of Android apps exist in various app markets such as GooglePlay, AppChina, Huawei app store, etc. The success of Android has attracted the interest of hackers who build thousands and even millions of Android malware that are distributed over the app markets. Detecting such malware is thus of the utmost importance to preserve the security and privacy of billions of Android app users. In this thesis, the author focuses on three main research challenges. First, the author investigates the problem of Android malware labeling. This is a key challenge that needs to be overcome to build reference datasets, to then be able to use AI techniques. Second, he investigates the impact of obfuscation and deobfuscation on the detection of piggybacked apps. Finally, he studies the problem of generating relevant testing samples (i.e., adversarial samples) to assess antivirus products.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/158775/2/02whole.pdf
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	au.edu.uts.lib/ppc
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Machine Learning Detection and Analysis on Obfuscated Android Malware	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

Android is the leading mobile operating system, accounting for more than 80% of the market share. Millions of Android apps exist in various app markets such as GooglePlay, AppChina, Huawei app store, etc. The success of Android has attracted the interest of hackers who build thousands and even millions of Android malware that are distributed over the app markets. Detecting such malware is thus of the utmost importance to preserve the security and privacy of billions of Android app users. In this thesis, the author focuses on three main research challenges. First, the author investigates the problem of Android malware labeling. This is a key challenge that needs to be overcome to build reference datasets, to then be able to use AI techniques. Second, he investigates the impact of obfuscation and deobfuscation on the detection of piggybacked apps. Finally, he studies the problem of generating relevant testing samples (i.e., adversarial samples) to assess antivirus products.

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http://hdl.handle.net/10453/158775