A Novel Android Malware Detection Method Based on Visible User Interface

Tan, S; Tian, Z; Zhong, X; Yu, S; Zhang, W; Dong, G

A Novel Android Malware Detection Method Based on Visible User Interface

Tan, S Tian, Z

Zhong, X Yu, S

Zhang, W Dong, G

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021, 2022, 00, pp. 659-666
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Tan, S
dc.contributor.author	Tian, Z https://orcid.org/0000-0001-8905-0941
dc.contributor.author	Zhong, X
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.contributor.author	Zhang, W
dc.contributor.author	Dong, G
dc.contributor.editor	Zhao, L
dc.contributor.editor	Kumar, N
dc.contributor.editor	Hsu, RC
dc.contributor.editor	Zou, D
dc.date	2021-10-20
dc.date.accessioned	2023-04-06T05:25:53Z
dc.date.available	2023-04-06T05:25:53Z
dc.date.issued	2022-01-01
dc.identifier.citation	Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021, 2022, 00, pp. 659-666
dc.identifier.isbn	9781665416580
dc.identifier.issn	2324-898X
dc.identifier.issn	2324-9013
dc.identifier.uri	http://hdl.handle.net/10453/169353
dc.description.abstract	Machine learning has been increasingly adopted to detect Android malwares. Most existing studies depend on features in code space such as information flows and API calls. Malware variants would engage these models in a never-ending war. Inspired by the observation that some variants share similar or even identical user interfaces (UIs), this paper explores employing visible UI screenshot as the indicator to build a novel Android malware detection method. To achieve this vision, we built the first Android Application Screenshot Dataset (AnASD) consisting of more than twenty thousand UI screenshots produced by both benign applications and malwares. A thorough analysis was conducted to characterize the dataset, especially the UI difference between benign applications and malwares. Then a set of state of the art deep learning classifiers on AnASD were trained and evaluated. The results of both sim-ilarity measurement and classification performance proved the feasibility to detect Android malwares based on user interfaces. To facilitate the research community, the dataset is free available at https://doi.org/10.6084/m9.figshare.14445768.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021
dc.relation.ispartof	IEEE International Conference on Trust, Security and Privacy in Computing and Communications
dc.relation.ispartofseries	IEEE International Conference on Trust Security and Privacy in Computing and Communications
dc.relation.isbasedon	10.1109/TrustCom53373.2021.00098
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Novel Android Malware Detection Method Based on Visible User Interface
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Shenyang, China
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-04-06T05:25:52Z
pubs.finish-date	2021-10-22
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2021-10-20
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

Machine learning has been increasingly adopted to detect Android malwares. Most existing studies depend on features in code space such as information flows and API calls. Malware variants would engage these models in a never-ending war. Inspired by the observation that some variants share similar or even identical user interfaces (UIs), this paper explores employing visible UI screenshot as the indicator to build a novel Android malware detection method. To achieve this vision, we built the first Android Application Screenshot Dataset (AnASD) consisting of more than twenty thousand UI screenshots produced by both benign applications and malwares. A thorough analysis was conducted to characterize the dataset, especially the UI difference between benign applications and malwares. Then a set of state of the art deep learning classifiers on AnASD were trained and evaluated. The results of both sim-ilarity measurement and classification performance proved the feasibility to detect Android malwares based on user interfaces. To facilitate the research community, the dataset is free available at https://doi.org/10.6084/m9.figshare.14445768.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/169353