著者

Sun Bo Luo Xiapu Akiyama Mitsuaki Watanabe Takuya Mori Tatsuya

出版者

一般社団法人 情報処理学会

雑誌

Journal of Information Processing

巻号頁・発行日

vol.26, pp.212-223, 2018

被引用文献数

3

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<p>Mobile app stores, such as Google Play, play a vital role in the ecosystem of mobile device software distribution platforms. When users find an app of interest, they can acquire useful data from the app store to inform their decision regarding whether to install the app. This data includes ratings, reviews, number of installs, and the category of the app. The ratings and reviews are the <i>user-generated content</i> (UGC) that affect the reputation of an app. Therefore, <i>miscreants</i> can leverage such channels to conduct <i>promotional attacks</i>; for example, a miscreant may promote a malicious app by endowing it with a good reputation via fake ratings and reviews to encourage would-be victims to install the app. In this study, we have developed a system called <i>PADetective</i> that detects miscreants who are likely to be conducting promotional attacks. Using a 1723-entry labeled dataset, we demonstrate that the true positive rate of detection model is 90%, with a false positive rate of 5.8%. We then applied our system to an unlabeled dataset of 57M reviews written by 20M users for 1M apps to characterize the prevalence of threats in the wild. The PADetective system detected 289K reviewers as potential PA attackers. The detected potential PA attackers posted reviews to 136K apps, which included 21K malicious apps. We also report that our system can be used to identify potentially malicious apps that have not been detected by anti-virus checkers.</p>