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AuthorHe D.
AuthorHong K.
AuthorCheng Y.
AuthorTang Z.
AuthorGuizani M.
Available date2020-04-27T08:34:19Z
Publication Date2019
Publication NameIEEE Wireless Communications
ISSN15361284
URIhttp://dx.doi.org/10.1109/MWC.2019.1800322
URIhttp://hdl.handle.net/10576/14557
AbstractApp markets play an important role in distributing various apps to mobile users. The app market vendors provide reputation systems to assist users in finding useful and reputable apps by ranking them. Unfortunately, there are signs that an app's ranking can be easily manipulated, which causes unfair competition for those highly ranked ones. Here we propose a novel approach based on deep learning to detect such malicious ranking manipulations. The proposed neural network has a novel architecture that is able to incorporate a variety of features designed from the publicly available application information in the app market. We have conducted extensive experiments as well as individual case analysis and the results demonstrate the effectiveness of our proposed approach. - 2002-2012 IEEE.
SponsorAcknowledgMent This research is supported by the National Science Foundation of China (Grants: U1636216 and 51477056); the National Key R&D Program of China (2017YFB0801701 and 2017YFB0802805); a special project of the Shanghai Science and Technology Commission on Technical Standards (No.16DZ0503000); and the Fundamental Research Funds for the Central Universities. Daojing He is the corresponding author of this article.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectReviews
SubjectData mining| Deceptive reviews
SubjectCompetition
SubjectDeep learning
TitleDetecting Promotion Attacks in the App Market Using Neural Networks
TypeArticle
Pagination110-116
Issue Number4
Volume Number26


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