Enhancing user experience by extended Fraud Detection ranking approach

Abstract

Dishonest activities in App Store, the most favoured app market, propellant search rank mishandle and lead to malware growth. To recognize malware, earlier effort has made awareness on app executable and agreement scrutiny. Here, we initiate SSPA, a novel system that determines and controls suggestions left behind by fraudsters, to detect both malware and apps focused to their content fraud. SSPA demonstrated the reliability and individually combines perceived apps with linguistic and behavioural signals gathered from app store to identify apprehensive apps. SSPA accomplishes over 95% accuracy in classifying gold standard datasets of malware, deceptive and legal apps. SSPA realizes hundreds of deceptive apps that currently avoid App Store detection technology. SSPA also helps to determine the malware add-on to the existing and popular apps that implemented a specific kind of verification approach.