DETECTION OF HTTP FLOOD ATTACKS BASED ON MACHINE LEARNING ALGORITHMS
Norbek Karimov, Furkat Rakhmatov, Oybek Xolmuminov · Himičeskaâ tehnologiâ. Kontrolʹ i upravlenie/Chemical Technology. Control and Management · 2025
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.