Fraudulent E-Commerce Websites Detection Through Machine Learning

  1. Manuel Sánchez-Paniagua 12
  2. Eduardo Fidalgo 12
  3. Enrique Alegre 12
  4. Francisco Jáñez-Martino 12
  1. 1 Universidad de León
    info

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

  2. 2 INCIBE (Spanish National Cybersecurity Institute, León)
Libro:
Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings
  1. Hugo Sanjurjo González (coord.)
  2. Iker Pastor López (coord.)
  3. Pablo García Bringas (coord.)
  4. Héctor Quintián (coord.)
  5. Emilio Corchado (coord.)

Editorial: Springer International Publishing AG

ISBN: 978-3-030-86271-8,  978-3-030-86270-1

Año de publicación: 2021

Páginas: 267-279

Congreso: Hybrid Artificial Intelligent Systems (HAIS) (16. 2021. Bilbao)

Tipo: Aportación congreso

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Resumen

With the emergence of e-commerce, many users are exposed to fraudulent websites, where attackers sell counterfeit products or goods that never arrive. These websites take money from users, but also they can stole their identity or credit card information. Current applications for user protection are based on blacklists and rules that turn out into a high false-positive rate and need a continuously updating. In this work, we built and make publicly available a suspicious of being fraudulent website dataset based on distinctive features, including seven novel features, to identify these domains based on recently published approaches and current web page properties. Our model obtained up to 75% F1- Score using Random Forest algorithm and 11 hand-crafted features, on a 282 samples dataset.

Fuente de los datos: Dialnet