Anomaly Detection based on Review Burstness and Ranking Fraud Discovery

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Anpu Alexander, Rahila N. A., P. Mohamed Shameem

Anpu Alexander, Rahila N A. and Mohamed P Shameem. Anomaly Detection based on Review Burstness and Ranking Fraud Discovery. International Journal of Computer Applications 169(9):44-47, July 2017. BibTeX

	author = {Anpu Alexander and Rahila N. A. and P. Mohamed Shameem},
	title = {Anomaly Detection based on Review Burstness and Ranking Fraud Discovery},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2017},
	volume = {169},
	number = {9},
	month = {Jul},
	year = {2017},
	issn = {0975-8887},
	pages = {44-47},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2017914883},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Nowadays everyone is using smart phone. Many applications are in smart phone. To download an application user visit App store such as Google play store, Apple play store etc, then he or she is able to see the different application lists. User has no awareness about the application. So user looks at the list and download the application from App Store based on the mobile app rank. App developers use different ways to promote their Apps in order to get top position in App store for example, high rating and good reviews are given about the mobile app i.e. there is fraud behavior occur it. To detect fraud behavior first identify the active periods of mobile app, namely leading session of mobile apps. In the existing system the leading event and leading session of an app identified from the collected historical records. Then ranking based evidence, rating based evidence and review based evidence were collected from the historical records. These evidence score value is used to detect fraud behavior occur in the mobile app. In proposed system from the reviews of mobile app it identifies if it is a fake review or not.


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Aggregation, Leading session, SVM.