Uncovering Hidden Gambling Networks in the Philippines Using AI-Driven Web Similarity Analysis

Abstract

The rapid expansion of online gambling in the Philippines has made it difficult for regulators to keep track of unlicensed operations. 
This presentation introduces an AI- and ML-powered framework that identifies related or cloned gambling sites through web 
similarity analysis. This helps track and block these illegal gambling sites as soon as they come online.

The system analyses digital fingerprints to build feature vectors representing each domain’s online identity. Using similarity 
algorithms, the framework uncovers networks of domains sharing common infrastructure or content lineage. Application of this 
approach led to the discovery of a significant number of previously unlisted or disguised gambling sites linked to known operators. 
The session details the feature engineering process, pipeline automation, and real-world findings, demonstrating how AI-driven web 
similarity analysis enhances cyber threat intelligence and strengthens regulatory oversight of the online gambling ecosystem.