Fraudulent E-Commerce Websites Detection Through Machine Learning
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Manuel Sánchez-Paniagua [1] [2] ; Eduardo Fidalgo [1] [2] ; Enrique Alegre [1] [2] ; Francisco Jáñez-Martino [1] [2]
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[1] Universidad de León
Universidad de León
León, España
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[2] INCIBE (Spanish National Cybersecurity Institute, León)
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- Localización: Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings / coord. por Hugo Sanjurjo González, Iker Pastor López, Pablo García Bringas, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2021, ISBN 978-3-030-86271-8, págs. 267-279
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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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.