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2016 | OriginalPaper | Buchkapitel

The Usability of Metadata for Android Application Analysis

verfasst von : Takeshi Takahashi, Tao Ban, Chin-Wei Tien, Chih-Hung Lin, Daisuke Inoue, Koji Nakao

Erschienen in: Neural Information Processing

Verlag: Springer International Publishing

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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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Fußnoten
1
We used the language-detection library [2] to detect the language, stemmify [7] for the stemming operation, and stoplist/en.txt of MALLET [5] as the list of stop words.
 
2
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.”
 
3
We used the “kmeans” [4] function of Ruby gem [8].
 
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Metadaten
Titel
The Usability of Metadata for Android Application Analysis
verfasst von
Takeshi Takahashi
Tao Ban
Chin-Wei Tien
Chih-Hung Lin
Daisuke Inoue
Koji Nakao
Copyright-Jahr
2016
DOI
https://doi.org/10.1007/978-3-319-46687-3_60