Aurangzeb, Sana, Aleem, Muhammad, Khan, Muhammad Taimoor ORCID: https://orcid.org/0000-0002-5752-6420, Loukas, George ORCID: https://orcid.org/0000-0003-3559-5182 and Sakellari, Georgia ORCID: https://orcid.org/0000-0001-7238-8700 (2024) AndroDex: Android Dex images of obfuscated malware. Scientific Data, 11 (212). pp. 1-10. ISSN 2052-4463 (Online) (doi:10.1038/s41597-024-03027-3)

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Abstract

With the emergence of technology and the usage of a large number of smart devices, cyber threats are increasing. Therefore, research studies have shifted their attention to detecting Android malware in recent years. As a result, a reliable and large-scale malware dataset is essential to build effective malware classifiers. In this paper, we have created AndroDex: an Android malware dataset containing a total of 24,746 samples that belong to more than 180 malware families. These samples are based on .dex images that truly reflect the characteristics of malware. To construct this dataset, we first downloaded the APKs of the malware, applied obfuscation techniques, and then converted them into images. We believe that this dataset will significantly enhance a series of research studies, including Android malware detection and classification, and it will also boost deep learning classification efforts, among others. The main objective of creating images based on the Android dataset is to help other malware researchers better understand how malware works. Additionally, an important result of this study is that most malware nowadays employs obfuscation techniques to hide their malicious activities. However, malware images can overcome such issues. The main limitation of this dataset is that it contains images based on .dex files that is based on static analysis. However, dynamic analysis takes time, therefore, to overcome the issue of time and space this dataset can be used for the initial examination of any .apk files.

Item Type:	Article
Uncontrolled Keywords:	malware; Android; cyber attacks
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	20 Feb 2024 14:18
URI:	http://gala.gre.ac.uk/id/eprint/45644

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