DDoS Attack Detection Using Machine Learning

This paper presents a practical application of machine learning techniques for the detection of Distributed Denial of Service (DDoS) attacks in network traffic. The goal of the research is to evaluate the effectiveness of selected classification models, which are Logistic Regression, Decision Tree, Random Forest, and K-Nearest Neighbors, on the publicly available APA-DDoS dataset. The study involves a complete pipeline, including data preprocessing, model training, parameter optimization, and performance evaluation using metrics such as accuracy, precision, recall, F1-score, and cross-validation. Among the tested models, Random Forest achieved the highest classification performance and stability, demonstrating its suitability for real world deployment. The results indicate that machine learning provides a viable and adaptive approach to enhancing cybersecurity against evolving DDoS threats.