Detection of fraudulent and malicious websites by analysing user reviews for online shopping websites

Asha Manek, S. and Deepa Shenoy, P. and Chandra Mohan, M. and Venugopal, K.R. (2016) Detection of fraudulent and malicious websites by analysing user reviews for online shopping websites. International Journal of Knowledge and Web Intelligence, 5 (3). pp. 171-189.

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Official URL: http://10.1504/IJKWI.2016.078712

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 …

Item Type: Article
Uncontrolled Keywords: fake reviews, malicious websites, supervised learning, sentiment analysis, Bayesian logistic regression, fraud detection, user reviews, online shopping websites
Subjects: Faculty of Engineering > Computer Science & Information Science Engineering
Divisions: University Visvesvarayya College of Engineering > Department of Computer Science and Information Science Engineering
Depositing User: Ms Laxmi Kamble
Date Deposited: 15 Sep 2021 10:13
Last Modified: 15 Sep 2021 10:13
URI: http://eprints-bangaloreuniversity.in/id/eprint/9559

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