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Fraudulence Detection and Recommendation of Trusted Websites

  • N. C. SenthilkumarEmail author
  • J. Gitanjali
  • A. Monika
  • R. Monisha
Conference paper
  • 18 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1054)

Abstract

We live in the world of science where everything could be done possibly through online facilities. As the online services increase, the fraudulent sites also increase, however. Therefore, it enhances the difficulty of people to identify a fake or fraudulent or scam website. Fraudsters are highly intelligent in making or persuading fake products which seem exactly same as the original. Some scam websites use low prices to attract people. Most of the fraudulent sites use domain names that reference a popular brand or product name and design with mere change. Some websites will get your card details before you intend to buy a product. Fraudsters try to attract people’s attention by advertising the fake products in many social websites such as Facebook, Instagram and Twitter, and when people wishes to view about the product and its details, they are forced to install an app of that site. So, here we have a solution to find those fraudulent products and recommend some trusted products for people to buy with good quality and at a fair price. We use the reviews or feedback or comments and the rating from the users who already purchased the product and shared their experience for identifying fraudulent products and giving a caution to the remaining people. This paper concentrates on analyzing the reviews and extracting the useful information to guide the customers using text mining. As a result of this, people will get a better idea of making a proper decision in buying the product through online.

Keywords

Decision making Fraudulent Feedback Rating Recommendation Reviews Text mining 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • N. C. Senthilkumar
    • 1
    Email author
  • J. Gitanjali
    • 1
  • A. Monika
    • 1
  • R. Monisha
    • 1
  1. 1.School of Information TechnologyVellore Institute of TechnologyVelloreIndia

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