Geodesic
Anomalous Traffic Identification Method for POST Messages Based on Gambling Website Templates
Computer Science and Information Systems, Tome 22 (2025) no. 1.

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Malicious websites pose significant social risks, necessitating automatic, efficient, and accurate identification methods. This paper proposes a POST traffic classification method based on website templates to identify abnormal traffic from gambling websites. Using Fiddler, POST message data is collected from several gambling sites, extracting features like URLs, cookie parameters, and request body parameters to create a Gambling Website Single POST Message Dataset (GSPD). These features are converted into vector representations with Word2Vec and TF-IDF techniques. Hierarchical clustering identifies template-generated types, achieving unsupervised template recognition. Using clustering results, individual POST messages are labeled and features are extracted using TF-IDF and mutual information methods. The parameters of a Support Vector Machine (SVM) are then optimized with the Particle Swarm Optimization (PSO) algorithm for optimal classification. Experimental results show the model’s excellent performance, with a test set accuracy of 0.9985 and high precision, recall, and F1-scores, effectively identifying gambling and other illegal websites.
Keywords: Template recognition, Illegal Website Detectio, feature extraction, POST traffic classification 
@article{CSIS_2025_22_1_a4,
     author = {Zhimin Feng and Dezhi Han and Songyang Wu and Wenqi Sun and Shuxin Shi},
     title = {Anomalous {Traffic} {Identification} {Method} for {POST} {Messages} {Based} on {Gambling} {Website} {Templates}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {22},
     number = {1},
     year = {2025},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2025_22_1_a4/}
}
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Zhimin Feng; Dezhi Han; Songyang Wu; Wenqi Sun; Shuxin Shi. Anomalous Traffic Identification Method for POST Messages Based on Gambling Website Templates. Computer Science and Information Systems, Tome 22 (2025) no. 1. http://geodesic.mathdoc.fr/item/CSIS_2025_22_1_a4/