Mobile App market refers to fraudulent or deceptive activities which have a purpose of bumping up the Apps in the popularity list. It becomes more frequent for App developers to use adumbral means, such as inflating their Apps’ sales or posting phony App ratings, to commit Review cheats. While the importance of preventing ranking cheat has been widely recognized, there is limited understanding and research in this area. We propose a new algorithm for this kind of the problem using Marshal Classification scam identification technique for mobile Apps. Specifically, we first propose to accurately locate the ranking fraud by mining the active periods, namely leading sessions, of mobile Apps. Such leading sessions can find out the local anomaly instead of global anomaly of App rankings. Furthermore, we investigate three types of evidences, i.e., ranking based evidences, rating based evidences and review based evidences, by modelling Apps’ ranking, valuation, review and behaviours through analytical detection principle tests using Marshal Classification Scan Analysis Technique.

Mobile Apps, ranking fraud detection, evidence aggregation, historical ranking records, rating and review

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