A Machine Learning Based Approach for Mobile App Rating Manipulation Detection

Song, Yang and Wu, Chen and Zhu, Sencun and Wang, Haining (2019) A Machine Learning Based Approach for Mobile App Rating Manipulation Detection. EAI Endorsed Transactions on Security and Safety, 5 (18). e3. ISSN 2032-9393

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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.

Item Type: Article
Uncontrolled Keywords: Machine Learning, App Store, Rating Manipulation, Attack Detection
Subjects: H Social Sciences > H Social Sciences (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor IV
Date Deposited: 26 Mar 2021 13:59
Last Modified: 26 Mar 2021 13:59
URI: https://eprints.eudl.eu/id/eprint/2104

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