Identification and Detection of Illegal Gambling Websites and Analysis of User Behavior

Abstract

Illegal gambling websites use advanced technology to evade regulations, posing cybersecurity challenges. To address this, we propose a machine learning method to identify these sites and analyze user behavior accurately. The method extracts key data from post messages in a real-world network environment, generating word vectors via Word2Vec with TF-IDF, which are then downscaled and feature-extracted using a Stacked Denoising Auto Encoder (SDAE). Next, this paper uses Agglomerative Clustering, improved through a combination of distance caching and heap optimization, to initially cluster post-template websites of the same type by clustering them into the same cluster. Then, multiple algorithms are integrated within each website cluster to cluster users’ different operational behav-iors into different clusters based on the cosine similarity consensus function voting secondary clustering. Results show improved detection of illegal gambling sites and classification of user activities, offering new insights for combating these sites.