Beyond the Brink: Safeguarding Digital Landscapes from DDoS Onslaughts with Machine Learning

The accelerating pace of digitization has enabled seamless global connectivity but also exposed systems to disruptive cyber threats.Among these, Distributed Denial-of-Service (DDoS) attacks remain one of the most severe, capable of crippling organizations by overwhelming services with illegitimate traffic.Traditional defense mechanisms, which often rely on fixed signatures, struggle against adaptive and sophisticated attack patterns.This paper investigates the role of machine learning (ML) in detecting DDoS attacks, with a focus on Logistic Regression, Random Forest, and XGBoost.Using real-world datasets, we evaluate model performance in terms of accuracy, precision, recall, and scalability.Results demonstrate that ensemble-based models such as Random Forest and XGBoost outperform baseline classifiers, offering resilience against evolving attack strategies.Our findings highlight the promise of adaptive ML frameworks as a foundation for strengthening cybersecurity in cloud computing, IoT, and 5G ecosystems.