A systematic review of DoS and DDoS attack detection in cloud environments: evolutionary trends and the emerging role of meta-learning for cross-domain adaptability

A systematic review of DoS and DDoS attack detection in cloud environments: evolutionary trends and the emerging role of meta-learning for cross-domain adaptability. Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks continue to be one of the most persistent and rapidly evolving threats to current network infrastructures as they are becoming more sophisticated and increasingly disrupting the availability, reliability and security of cloud-based systems, Software-Defined Networks (SDNs), Internet of Things (IoT) ecosystems and next generation communication environments. This paper provides a systematic review of the literature on DoS and DDoS attack detection and mitigation, covering the various stages of evolutions in the field from early statistical and anomaly-based methods to adaptive machine learning and deep learning, as well as domain specific detection methods. The review, which is based on a thorough examination of 120 papers (114 primary studies and 6 supplementary studies), provides a synthesis of the latest advances in network and application-layer detection, protocol-specific detection frameworks, cloud-based intrusion detection systems, SDN-enabled programmable detection, IoT/Fog/Edge architectures, ensemble and hybrid models, transfer learning, and blockchain-based security mechanisms. A systematic review of benchmark datasets, feature engineering techniques and comparative performance shows that current deep learning systems attain almost perfect classification rates in experimental environments, but fundamental limitations in their generalizability, computational efficiency, preservation of privacy and explainability remain a constraint for their deployment in real-world environments. Importantly, this is a lot of work, but less than 6 % of the reviewed studies (seven out of 120) use meta-learning, transfer learning or few-shot approaches explicitly, which reflects the ongoing lack of capability in the field of adaptive generali…