Ainebyoona Patrick and Adeleke Raheem Ajiboye, Kampala International University, Uganda

Distributed Denial of Service (DDoS) attacks have become some of the most common and damaging cyberthreats in our increasingly connected world. This literature review explores recent developments in using machine learning algorithms to detect DDoS intrusions, with a special emphasis on approaches that fine-tune self-updating parameters. By bringing together insights from multiple recent studies. This review examines a variety of machine learning methods such as Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbours (KNN). It looks at the strengths and weaknesses of each technique and discusses how best to integrate them with the existing security infrastructure. Particular attention is given to self-updating models that can quickly adapt to new and evolving attack patterns. The paper also reviews performance metrics, important considerations around datasets, and outlines future research directions in this fast-moving area. Overall, the findings indicate that adaptive, self-updating machine learning models outperform static ones in detecting complex DDoS attacks, with Random Forest approaches consistently delivering strong results across various studies.

DDoS detection, self-updating algorithms, Adaptive Parameter Calibration, Intrusion Detection Systems. Machine learning.