Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74386
Title: Characterizing promotional attacks in mobile app store
Authors: Sun, B
Luo, X 
Akiyama, M
Watanabe, T
Mori, T
Keywords: Machine learning
Mobile app store
Promotional attacks
Issue Date: 2017
Publisher: Springer
Source: Communications in computer and information science, 2017, v. 719, p. 113-127 How to cite?
Journal: Communications in computer and information science 
Abstract: Mobile app stores, such as Google Play, play a vital role in the ecosystem of mobile apps. When users look for an app of interest, they can acquire useful data from the app store to facilitate their decision on installing the app or not. This data includes ratings, reviews, number of installs, and the category of the app. The ratings and reviews are the user-generated content (UGC) that affect the reputation of an app. Unfortunately, miscreants also exploit such channels to conduct promotional attacks (PAs) that lure victims to install malicious apps. In this paper, we propose and develop a new system called PADetective to detect miscreants who are likely to be conducting promotional attacks. Using a dataset with 1,723 of labeled samples, we demonstrate that the true positive rate of detection model is 90%, with a false positive rate of 5.8%. We then applied PADetective to a large dataset for characterizing the prevalence of PAs in the wild and find 289 K potential PA attackers who posted reviews to 21 K malicious apps.
Description: 8th International Conference on Applications and Techniques in Information Security, ATIS 2017, 6 - 7 July 2017
URI: http://hdl.handle.net/10397/74386
ISBN: 9789811054204
ISSN: 1865-0929
EISSN: 1865-0937
DOI: 10.1007/978-981-10-5421-1_10
Appears in Collections:Conference Paper

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