Abstract

This research covers a detailed survey of the Distributed Denial of Service (DDoS) attack cases in the context of cloud computing, describing their evolution in light of the past, current, and future trends, as well as the related technologies used in detection and mitigation approaches. It highlights the contradictory nature of cloud computing services to be both a  for resource scalability and flexibility and a hurmful, as any DDoS attack can wreak havoc on it. The literature has been synthesized over the past decade, from 2012 to 2024, to identify the main advances in detection techniques, starting with traditional signature-based methods and ending with AI-based innovative approaches, such as machine learning and deep learning algorithms. The paper deals with the DDoS challenges, including the evolution of attack vectors, scalability limit of detection systems, integration of DDoS with cloud services, false positives or negatives, and resource limitations. Future research and practice call for the adoption of AI and machine learning techniques, a mixture of manual and automatic detection approaches, strong collaborations, regular system updates, and user education and awareness.