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DETECTION OF HTTP FLOOD ATTACKS BASED ON MACHINE LEARNING ALGORITHMS

Norbek KarimovTashkent region pedagogical skills center, Tashkent region, Uzbekistan. E-mail: [email protected];Furkat RakhmatovTashkent region pedagogical skills center, Tashkent region, Uzbekistan. E-mail: [email protected];Oybek XolmuminovTashkent University of Information Technologies named after Muhammad al-Khwarizmi. Address: Tashkent 100084, Amir Temur Avenue 108. Tashkent city, Uzbekistan. E-mail: [email protected]

Himičeskaâ tehnologiâ. Kontrolʹ i upravlenie/Chemical Technology. Control and Managementjournal2025en

ABI

Annotatsiya

This paper analyzes the effectiveness of Random Forest and SVM models for detecting HTTP Flood attacks. Experimental results demonstrate that both models achieve high accuracy. Evaluation was conducted using Precision, Recall, and F1 Score metrics. Additionally, key features of network traffic were extracted through correlation analysis to enable real-time application of the models in attack detection. The findings provide important insights into detecting DDoS attacks using machine learning and improving model performance.

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Mavzular

Network Security and Intrusion Detection Anomaly Detection Techniques and Applications

Identifikatorlar

DOI: 10.59048/2181-1105.1663

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