A novel mechanism for fraud rank detection in social networks

Abstract

Fraudulent behavior in the Play Store, Device’s most social media app market, boosts search rankings misuse, and malicious software prevalence. Earlier work focused on malware detection executable applications and authorization investigation. The proposed Fraud rank detector discovers and uses evidence that fraudsters have left behind to detect malware and applications that are vulnerable to malware Manipulation of the hunt. Fraud Rank Detector measures review patterns and integrate uniquely identified user interactions with fraudulent and behavioral metrics gleaned from Fraud Rank Detector details the comments obtained to recognize suspect applications. Fraud Rank Detector attains more than the 95 basis points of precision in malware categorization, Fraudulent and legitimate gold standard data applications. We indicate that the Fraud Rank Detector finds the apps that oversee the search security guard’s tracking technology.