A Machine Learning Based Approach for Mobile App Rating Manipulation Detection
Keywords:Machine Learning, App Store, Rating Manipulation, Attack Detection
In order to promote apps in mobile app stores, for malicious developers and users, manipulating average rating is a popular and feasible way. In this work, we propose a two-phase machine learning approach to detecting app rating manipulation attacks. In the ﬁrst learning phase, we generate feature ranks for diﬀerent app stores and ﬁnd that top features match the characteristics of abused apps and malicious users. In the second learning phase, we choose top N features and train our models for each app store. With cross-validation, our training models achieve 85% f-score. We also use our training models to discover new suspicious apps from our data set and evaluate them with two criteria. Finally, we conduct some analysis based on the suspicious apps classiﬁed by our training models and some interesting results are discovered.
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National Science Foundation
Grant numbers CNS-1618684