Application of machine learning to identify Counterfeit Website

Kuan Ting Wu, Shing Hua Chou, Shyh Wei Chen, Ching Tsorng Tsai, Shyan-Ming Yuan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Recent years the prevalence of fraudulent websites has become more severe than before. Fraudulent ecommerce websites that sell counterfeit goods not only cost financial damage to consumers but also have a great impact on Internet industry. Nowadays, there is not an effective way to confront these websites. In this paper, we look forward to achieving three goals: find the characteristics of counterfeit websites, train models for classifying ecommerce websites and provide a service to help consumers distinguish counterfeit websites from legitimate ones.

Original languageEnglish
Title of host publication20th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2018 - Proceedings
EditorsGabriele Anderst-Kotsis, Eric Pardede, Matthias Steinbauer, Maria Indrawan-Santiago, Ivan Luiz Salvadori, Ivan Luiz Salvadori, Ismail Khalil
PublisherAssociation for Computing Machinery
Pages321-324
Number of pages4
ISBN (Electronic)9781450364799
DOIs
StatePublished - 19 Nov 2018
Event20th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2018 - Yogyakarta, Indonesia
Duration: 19 Nov 201821 Nov 2018

Publication series

NameACM International Conference Proceeding Series

Conference

Conference20th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2018
CountryIndonesia
CityYogyakarta
Period19/11/1821/11/18

Keywords

  • Counterfeit website
  • Decision tree
  • Fraudulent website
  • Logistic regression
  • Support vector machine

Fingerprint Dive into the research topics of 'Application of machine learning to identify Counterfeit Website'. Together they form a unique fingerprint.

  • Cite this

    Wu, K. T., Chou, S. H., Chen, S. W., Tsai, C. T., & Yuan, S-M. (2018). Application of machine learning to identify Counterfeit Website. In G. Anderst-Kotsis, E. Pardede, M. Steinbauer, M. Indrawan-Santiago, I. L. Salvadori, I. L. Salvadori, & I. Khalil (Eds.), 20th International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2018 - Proceedings (pp. 321-324). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3282373.3282407