For Project Enquiry +91 9962 067 067

EXISTING SYSTEM

PROPOSED SYSTEM

EXISTING CONCEPT:-

In existing learning methodologies studies of a recent trend, instead of relying on traditional marketing solutions, shady App developers resort to some fraudulent means to deliberately boost their Apps and eventually manipulate the chart rankings on an App store.

This is usually implemented by using so-called “bot farms” or “human water armies” to inflate the App downloads, ratings and reviews in a very short time

PROPOSED CONCEPT:

In this paper, we developed a ranking fraud detection system for mobile Apps. Specifically, we first showed that ranking fraud happened in leading sessions and provided method for mining leading sessions for each App from its historical ranking records.

 Then, we identified ranking  based evidences, rating based   evidences and review based evidences for detecting ranking fraud

EXISTING ALGORITHM:-

Multi-Scale Spam Detection

PROPOSED ALGORITHM:-

Mining Leading Sessions.

ALGORITHM DEFINITION:-

   Identified several representative behaviors of review spammers and model these behaviors to detect the spammers. we have studied the problem of detecting hybrid shilling attacks on rating data.

ALGORITHM DEFINITION:-

First, we need to discover leading events from the App’s historical ranking records. Second, we need to merge adjacent leading events for constructing leading sessions.

DRAWBACKS:-

When an app was promoted with help of ranking manipulation it could be top in leader board and more new users could be purchased that product effect other App reputation

The encouraging results show that the Existing algorithms are less effective for feature selection tasks of online applications.

ADVANTAGES:-

Detecting ranking fraud of mobile Apps is actually to detect ranking

Fraud within leading sessions of mobile Apps.

Contribute the methods to enhance

Fraud detection to improve the discovery of fraud in mobile apps.