Detection of fraudulent and malicious websites by analysing user reviews for online shopping websites. (2016)

• Record Type: Journal Article
• Authors: Manek, Asha S.; Shenoy, P. Deepa; Mohan, M. Chandra; Venugopal, K.R.
• Is Part Of: International journal of knowledge and web intelligence. Volume 5:Number 3(2016)
• Page Start: 171
• Page End: 189
• Publication Date: 2016
• Subjects: fake reviews -- malicious websites -- supervised learning -- sentiment analysis -- Bayesian logistic regression -- fraud detection -- user reviews -- online shopping websites; Information retrieval -- Periodicals; Web site development -- Periodicals; Internet programming -- Periodicals; 004.67805
• Languages: English
• ISSNs: 1755-8255

Abstract

Recently, the web has become a crucial worldwide platform for online shopping. People go online to sell and buy products, use online banking facilities and even give opinions about their online shopping experience. People with malicious intent may be involved in any online transaction with a fraudulent e-business give fake positive reviews that actually does not exist to promote or degrade the product. User reviews are extremely essential for decision making and at the same time cannot be reliable. In this paper, we propose a novel method Bayesian logistic regression classifier (BLRFier) that detects fraudulent and malicious websites by analysing user reviews for online shopping websites. We have built our own dataset by crawling reviews of benign and malicious e-shopping websites to apply supervised learning techniques. Experimental evaluation of BLRFier model achieved 100% accuracy signifying the effectiveness of this approach for real-life deployment.