Geodesic
Identification and Detection of Illegal Gambling Websites and Analysis of User Behavior
Computer Science and Information Systems, Tome 22 (2025) no. 3

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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 behaviors 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.
Keywords: Gambling websites, post messages, feature extraction, illegal website identification, cluster analysis 
Zhimin Zhang; Dezhi Han; Songyang Wu; Wenqi Sun; Shuxin Shi. Identification and Detection of Illegal Gambling Websites and Analysis of User Behavior. Computer Science and Information Systems, Tome 22 (2025) no. 3. http://geodesic.mathdoc.fr/item/CSIS_2025_22_3_a9/
@article{CSIS_2025_22_3_a9,
     author = {Zhimin Zhang and Dezhi Han and Songyang Wu and Wenqi Sun and Shuxin Shi},
     title = {Identification and {Detection} of {Illegal} {Gambling} {Websites} and {Analysis} of {User} {Behavior}},
     journal = {Computer Science and Information Systems},
     year = {2025},
     volume = {22},
     number = {3},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2025_22_3_a9/}
}
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