JAVA IEEE Search Rank Fraud and Malware Detection in Google Play

Abstract: Deceitful practices in Google Play, the most prominent Android application showcase, fuel look rank mishandle and malware multiplication. To distinguish malware, past work has concentrated on application executable and authorization examination. In this paper, we present FairPlay, a novel framework that finds and use follows left behind by fraudsters, to distinguish both malware and applications subjected to look rank misrepresentation. FairPlay corresponds audit exercises and remarkably consolidates recognized survey relations with semantic and behavioral signs gathered from Google Play application information (87 K applications, 2.9 M surveys, and 2.4M commentators, gathered over a large portion of a year), keeping in mind the end goal to distinguish suspicious applications. FairPlay accomplishes more than 95 percent precision in arranging best quality level datasets of malware, deceitful and honest to goodness applications. We demonstrate that 75 percent of the recognized malware applications take part in seek rank misrepresentation. FairPlay finds many fake applications that right now avoid Google Bouncer’s location innovation. FairPlay additionally helped the revelation of more than 1,000 audits, detailed for 193 applications, that uncover another kind of “coercive” survey battle: clients are badgering into composing positive surveys and introduce and survey different applications.

EXISTING SYSTEM

Deceitful practices in Google Android application showcase fuel seek rank manhandle and malware expansion. We exhibit FairPlay, a novel framework that reveals both malware and pursuit rank misrepresentation applications, by selecting trails that fraudsters abandon. To recognize suspicious applications, FairPlay PCF calculation connects audit exercises and extraordinarily joins identified survey relations with semantic and behavioral signs gathered from longitudinal Google Play application information.

PROPOSED SYSTEM

We contribute another longitudinal application dataset to the group, which comprises more than 87K applications, 2.9M audits, and 2.4M analysts gathered over a large portion of a year. FairPlay accomplishes more than exactness in arranging best quality level datasets of malware, fake and real applications. We demonstrate that of the distinguished malware applications take part in look rank misrepresentation. FairPlay finds many fake applications that right now avoid Google Bouncer recognition innovation, and uncovers another sort of assault battle, where clients are bothered into composing positive surveys, and introduce and audit different applications.

HARDWARE REQUIREMENTS: