Improvement of Distributed Denial of Service Attack Detection through Machine Learning and Data Processing
Fray L. Becerra-Suarez,
Ismael Fernández-Roman,
Manuel G. Forero
Abstract:The early and accurate detection of Distributed Denial of Service (DDoS) attacks is a fundamental area of research to safeguard the integrity and functionality of organizations’ digital ecosystems. Despite the growing importance of neural networks in recent years, the use of classical techniques remains relevant due to their interpretability, speed, resource efficiency, and satisfactory performance. This article presents the results of a comparative analysis of six machine learning techniques, namely, Random F… Show more
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