Abstract:The financial success of Android software stores like Google Play, as well as the incentive scheme they provide for popular apps, make them attractive targets for fraud and malicious behavior. Some fraudulent developers deceptively boost the search rank and popularity of their apps while malicious developers use app markets as a launch pad for their malware. In this project, introduce FairPlay, a novel system that discovers and leverages traces left behind by fraudsters, to detect both malware and apps subjected to search rank fraud. FairPlay correlates review activities and uniquely combines detected review relations with linguistic and behavioral signals gleaned from Google Play app data in order to identify suspicious apps. Adversaries can have chances to launch attacks by gathering victim’s information continuously. This project shows that an adversary can successfully infer a victim’s vertex identity and community identity by the knowledge of degrees within a time period. The project also includes a new supervised clustering algorithm to find groups of data (coarse and finer cluster). It directly incorporates the information of sample categories into the fraud clustering process. Keywords: -Android market, search rank fraud, malware detection.