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Journal of Gambling Studies
Erschienen in:

11.07.2024 | Original Research

Illegal Online Gambling Site Detection using Multiple Resource-Oriented Machine Learning

verfasst von: Moohong Min, Donggi Augustine Lee

Erschienen in: Journal of Gambling Studies | Ausgabe 4/2024

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Abstract

The COVID-19 pandemic has led to faster digitalization and illegal online gambling has become popular. As illegal online gambling brings not only financial threats but also breaches in overall cyber security, this study defines the concept of absolute illegal online gambling (AIOG) using a machine-learning-driven approach with information gathered from public webpages. By analysing 11,172 sites to detect illegal online gambling, the proposed model classifies key features such as URLs (Uniform Resource Locator), WHOIS, INDEX, and landing page information. With a combination of text and image analyses with machine learning-driven approach, the proposed model offers the ensemble combination of attributes for high detection performance with the verification of common attributes from metadata in online gambling. This study suggests a strategy for dynamic resource utilization to increase the classification accuracy of the current environment. As a result, this research expands the scope of hybrid web mining through constant updating of data to achieve content-based filtering.
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Metadaten
Titel
Illegal Online Gambling Site Detection using Multiple Resource-Oriented Machine Learning
verfasst von
Moohong Min
Donggi Augustine Lee
Publikationsdatum
11.07.2024
Verlag
Springer US
Erschienen in
Journal of Gambling Studies / Ausgabe 4/2024
Elektronische ISSN: 1573-3602
DOI
https://doi.org/10.1007/s10899-024-10337-z

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