Abstract
Distributed Denial of Service (DDoS) attacks pose a significant threat to the availability and performance of cloud computing systems. As these attacks continue to evolve in complexity and scale, traditional mitigation techniques may prove insufficient. This research explores the application of machine learning algorithms as an intelligent and adaptive approach to enhance DDoS detection and mitigation in cloud environments.The study leverages the dynamic and scalable nature of cloud computing to implement a robust defence mechanism against DDoS attacks. Machine learning models, such as supervised and unsupervised learning algorithms, are trained on network traffic data to identify patterns indicative of DDoS activity. The proposed system adapts to evolving attack strategies and is capable of real-time analysis, ensuring swift responses to emerging threats.
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