Detection Of Fraud Ranking For Mobile App Using IP Address Recognition Technique
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 4
Abstract
fraudulent activity in mobile app market means intruders or app developers use shady means to increase their app rating to bring their app in top 20 list to inflate their app sale. Knowledge engineering domain usually uses the methodologies to extract the useful knowledge from the given large data. Ongoing rapid growths of online data have created the need of KDD. Also ongoing rapid growth of online rating and review system to the app, make fraud app has been launched in the mobile market and let them be downloaded and used by many users. The fraud mobile app is not worth to use and wasting device memory. Sometimes such app is created with malicious software which is harmful to the device. To avoid this situation the fraud app should be find out. In existing work, fraud rankings are detected by applying the mining algorithm in app review. Local anomaly was detected instead of global anomaly. The analysis had been done reported. Human evaluators evaluated and produce the result. Time complexity is more to evaluate. To overcome this drawback, FRDS is proposed. To detect the fraud app, app’s reviews should be checked. To check whether the app reviews are fraud or not, the Fraud Ranking Detection System is proposed. In the mobile market, each mobile has its own unique IP Address. Hence, each user has unique IP Address. When giving the reviews to app, user IP Address is extracted by using the IP Address recognition technique. So that, from one IP Address number of reviews cannot be provided to same app. In this way fraud review is prevented in proposed work. This approach decreases the evaluation time of the result, hence it is efficient than the existing approach.
Authors and Affiliations
Esther Nowroji. S, Vanitha. S
Keywords
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