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

Moohong Min¹, Donggi Augustine Lee²

¹Department of Computer Education/Social Innovation Convergence Program, Sungkyunkwan University, Seoul, 03063, South Korea. iceo@skku.edu.

Journal of Gambling Studies

|July 11, 2024

Summary

This summary is machine-generated.

This study introduces a machine learning model to detect absolute illegal online gambling (AIOG) by analyzing website data. The AIOG detection method enhances cybersecurity and combats financial threats from illicit online activities.

Keywords:

Illegal online gambling Machine learning Web mining

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Area of Science:

Cybersecurity
Machine Learning
Web Mining

Background:

The COVID-19 pandemic accelerated digitalization, increasing the prevalence of illegal online gambling.
Illegal online gambling poses significant financial threats and cybersecurity risks.

Purpose of the Study:

To define and detect absolute illegal online gambling (AIOG) using a machine-learning-driven approach.
To analyze key website features for accurate AIOG classification.

Main Methods:

Analysis of 11,172 public webpages using a machine learning model.
Classification of features including URLs, WHOIS, INDEX, and landing page information.
Integration of text and image analysis with ensemble attribute combination.

Main Results:

The proposed model achieves high detection performance for AIOG.
Verification of common attributes from online gambling metadata enhances accuracy.
A strategy for dynamic resource utilization is suggested to improve classification.

Conclusions:

The research expands hybrid web mining techniques through continuous data updates.
Content-based filtering is achieved via constant data updating.
The developed model offers a robust solution for identifying illegal online gambling sites.