Detecting Malicious Android Game Applications on Third-Party Stores Using Machine Learning
Issued Date
2024-01-01
Resource Type
ISSN
23674512
eISSN
23674520
Scopus ID
2-s2.0-85191327710
Journal Title
Lecture Notes on Data Engineering and Communications Technologies
Volume
202
Start Page
238
End Page
251
Rights Holder(s)
SCOPUS
Bibliographic Citation
Lecture Notes on Data Engineering and Communications Technologies Vol.202 (2024) , 238-251
Suggested Citation
Sanamontre T., Visoottiviseth V., Ragkhitwetsagul C. Detecting Malicious Android Game Applications on Third-Party Stores Using Machine Learning. Lecture Notes on Data Engineering and Communications Technologies Vol.202 (2024) , 238-251. 251. doi:10.1007/978-3-031-57916-5_21 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/98201
Title
Detecting Malicious Android Game Applications on Third-Party Stores Using Machine Learning
Author(s)
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Due to Android’s flexibility in installing applications, it is one of the most popular mobile operating systems. Some Android users install applications from third-party stores even though they have the official application store, Google Play. These third-party stores usually have the mod version and the self-proclaimed original applications, which can be repackaged applications. Applications on these third-party stores can introduce security risks because of the non-transparent alteration and uploading processes. In this research, we inspect 492 Android applications from ten third-party stores for repackaged applications using information of APK files and a token-based code clone detection technique. We also classify repackaged applications as benign or malicious and categorize malicious applications into twelve malware categories. For the malware classification, we use machine learning techniques, including Random Forest, Decision Tree, and XGBoost, with the CCCS-CIC-AndMal-2020 Android malware dataset. Finally, we compare the results with VirusTotal, a well-known malware scanning website.
