Paper Details

As Mobile application plays an important role for all the smart phone users to play or perform different tasks.Mobile application developers are available in large number; they can develop the different mobile applications. Formaking lager users for their applications some developers involve in illegal activities. Due to these illegal activities themobile applications hires high rank in the application popularity list. Such fraudulent activities are used by more andmore application developers. The number of mobile applications has grown at a breathtaking rate over the past fewyears. Many people are downloading various applications from Apple’s App store and Google Play store withoutknowing that, weather these are genuine or not. To avoid this scenario, ranking fraud detection system for mobileapplications is proposed. It proposes to accurately locate the ranking fraud by mining the active periods, namely leadingsessions, of mobile applications. Such leading sessions can be leveraged for detecting the local anomaly instead of globalanomaly of application rankings. Furthermore, it investigates three types of evidences, which are ranking basedevidences, rating based evidences and review based evidences. In addition, it proposes an optimization basedaggregation method to integrate all the evidences for fraud detection. Finally, it evaluates the proposed system with realworld application data collected from the iOS App Store for a long time period. In the experiments, it validates theeffectiveness of the proposed system, and show the scalability of the detection algorithm as well as some regularity ofranking fraud activities.

Prof:Sulbha A. Ghadling, Ravina R. Sumbhe, Shradha S. Rakshe, Mrunalini V. Bhondve, Vijaya R. Lokhande, “Fraud Detection in Mobile Application”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 3, Issue 11, pp. 158-162, November 2016.