Abstract
The number of security incidents faced by Android users is growing, along with the surge in malware targeting Android terminals. Such malware arrives at the Android terminals in the form of Android Packages (APKs). Assorted techniques for protecting Android users from such malware have been reported, but most of them focus on the APK files themselves. Different from these approaches, we use metadata, such as web information obtained from the online APK markets, to improve the accuracy of malware identification. In this paper, we introduce malware detection schemes using metadata, which includes categories and descriptions of APKs. We introduce two types of schemes: statistical scheme and support vector machine-based scheme. Finally, we analyze and discuss the performance and usability of the schemes, and confirm the usability of web information for the purpose of identifying malware.
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Notes
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We used MALLET for running LDA and considered 300 topics because the MALLET documentation states that “The number of topics should depend to some degree on the size of the collection, but 200 to 400 will produce reasonably fine-grained results.”
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Takahashi, T., Ban, T., Tien, CW., Lin, CH., Inoue, D., Nakao, K. (2016). The Usability of Metadata for Android Application Analysis. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9947. Springer, Cham. https://doi.org/10.1007/978-3-319-46687-3_60
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