Publications

@article{sanchez-paniagua_fraudulent_2021,

title = {Fraudulent e-commerce websites detection through machine learning},

author = {Manuel Sánchez-Paniagua and Eduardo Fidalgo and Enrique Alegre and Francisco Jáñez-Martino},

url = {https://link.springer.com/chapter/10.1007/978-3-030-86271-8_23},

year  = {2021},

date = {2021-01-01},

journal = {Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021, Bilbao, Spain, September 22–24, 2021, Proceedings 16},

pages = {267–279},

abstract = {With the rise of e-commerce, users are increasingly vulnerable to fraudulent websites that sell counterfeit products or steal personal information. Existing protection methods, such as blacklists and rules, are prone to high false-positive rates and require constant updating. This paper presents a publicly available dataset of potentially fraudulent websites, incorporating seven new features for better detection. The model, using Random Forest and 11 handcrafted features, achieved an F1-Score of X on a dataset of 282 samples.},

note = {Publisher: Springer International Publishing},

keywords = {Cybersecurity, E-commerce, Fraud Detection, machine learning},

pubstate = {published},

tppubtype = {article}

}