Classification-Based Machine Learning For Detection of DDoS Attack In Cloud Computing

The proliferation of cloud computing has marked a pivotal shift in the delivery of Information Technology (IT) services, offering enhanced scalability and operational efficiency through on-demand access to remote data centers over the Internet. As cloud infrastructures continue to evolve, the imperative for robust security mechanisms becomes increasingly critical. These attacks not only disrupt traditional network environments but also present substantial challenges to emerging paradigms such as edge computing, cloud platforms, and the Internet of Things (IoT) [1], [2]. The proposed solution presents a promising avenue for fortifying the security of cloud computing by enabling the precise identification and timely mitigation of DDoS attacks. The utilization of classification algorithms not only facilitates the Perception of such attacks but also enhances the overall resilience of cloud-based services, ensuring uninterrupted access for legitimate users. These algorithms have achieved high accuracy rates ranging from 89.7% to 95% in different studies. The proposed method combining KNN, SVM, DT, NB and RF achieved the highest accuracy of 95%.