International Journal on Science and Technology

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Intelligent DDoS Detection Framework Using Random Forest and XGBoost

Author(s) Mr. Suresh Nannuri, Mr. Harish Reddy Gantla, Ms. Sindhuja M, Ms. Sri Kavya P, Mr. Sai Shiva Pal Reddy M, Ms. Preethi Sindhura T
Country India
Abstract Distributed Denial of Service (DDoS) attacks continue to be a major concern for network security, highlighting the need for effective detection methods. This study introduces a Python-based approach that leverages the Random Forest and XGBoost algorithms to identify and classify DDoS attacks. By analyzing numerical features from the CIC DDoS 2019 dataset, the system differentiates between benign and malicious network traffic. Unlike traditional methods that often depend on Decision Tree algorithms for their clarity but struggle with scalability and accuracy, Random Forest and XGBoost provide enhanced performance. These models are better suited for large-scale data, offering greater predictive accuracy and resilience against overfitting. The proposed system focuses on evaluating model accuracy, analyzing feature importance, and exploring potential for real-time deployment in cybersecurity. Preliminary results are anticipated to show marked improvements in detection rates, supporting the development of adaptive and dependable DDoS defense mechanisms.
Keywords DDoS Detection, Network Security, Random Forest, XGBoost, Machine Learning, CIC-DDoS2019, Intrusion Detection System, Feature Importance
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 17, Issue 2, April-June 2026
Published On 2026-05-08
DOI https://doi.org/10.71097/IJSAT.v17.i2.10754
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