T.S. Volokitina1, M.O. Tanygin2

1,2 South-Western State Universit (Kursk, Russia)
1 tativolokitina@gmail.com

A hybrid system for detecting phishing threats is presented, combining the advantages of URL blacklists and machine learning algorithms to ensure real-time protection of social networks. A comparative analysis of the effectiveness of four blacklist sources was conducted: Google Safe Browsing, PhishTank, OpenPhish, and the Roskomnadzor registry. It was established that update delays from 2 to 48 hours create a critical vulnerability window, during which 70% of successful attacks occur. The system architecture has been developed, providing combined use of operational blacklists and a heuristic classifier based on Random Forest for proactive detection of new threats. Experimental evaluation on a sample of 800 thousand records from the Odnoklassniki social network showed an increase in detection efficiency from 60 to 92% while reducing response time to 2 hours. The proposed system processes 100 thousand records per hour with a classification accuracy of 87%, ensuring the scalability of the solution for industrial application. Analysis of discriminative ability through AUC-ROC metric confirmed quantitative superiority of hybrid architecture with AUC value of 0.96 compared to 0.90 for blacklist combination and 0.93 for isolated classifier.

Volokitina T.S., Tanygin M.O. Hybrid real-time phishing detection system based on integration of blacklists and machine learning. Science Intensive Technologies. 2026. V. 27. № 3. P. 43−53. DOI: https://doi.org/10.18127/j19998465-202603-06 (in Russian)

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