Code and Supplementary Data for: Development of a Machine Learning-Based Framework for Real-Time Detection and Mitigation of DDoS Attacks

This archive contains the complete source code, configuration files, trained machine learning models, and monitoring scripts associated with the research article titled “Development of a Machine Learning-Based Framework for Real-Time Detection and Mitigation of DDoS Attacks.” The framework employs supervised learning algorithms, including Random Forest, XGBoost, and Multi-Layer Perceptron (MLP), trained on the CIC-DDoS2019 dataset. Real-time traffic capture is handled using Scapy, message queuing with Apache Kafka, and visualization through Flask and Plotly. The contents support reproducibility, performance evaluation, and deployment of the proposed DDoS detection system in experimental or production-like environments.Suitable for use in academic, research, and enterprise settings.