sesa 19(18): e3

Research Article

A Machine Learning Based Approach for Mobile App Rating Manipulation Detection

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  • @ARTICLE{10.4108/eai.8-4-2019.157415,
        author={Yang Song and Chen Wu and Sencun Zhu and Haining Wang},
        title={A Machine Learning Based Approach for Mobile App Rating Manipulation Detection},
        journal={EAI Endorsed Transactions on Security and Safety},
        volume={5},
        number={18},
        publisher={EAI},
        journal_a={SESA},
        year={2019},
        month={1},
        keywords={Machine Learning, App Store, Rating Manipulation, Attack Detection},
        doi={10.4108/eai.8-4-2019.157415}
    }
    
  • Yang Song
    Chen Wu
    Sencun Zhu
    Haining Wang
    Year: 2019
    A Machine Learning Based Approach for Mobile App Rating Manipulation Detection
    SESA
    EAI
    DOI: 10.4108/eai.8-4-2019.157415
Yang Song1, Chen Wu1, Sencun Zhu1,*, Haining Wang2
  • 1: Penn State University, University Park, PA 16802
  • 2: University of Delaware, Newark, DE 19716
*Contact email: sxz16@psu.edu

Abstract

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.