<scp>DDoS</scp> Detection and Mitigation in Cloud via Hybrid Architecture With Extractive Feature Set

ABSTRACT Unlike traditional systems, cloud infrastructures are dynamic and distributed, necessitating specialized methods to handle distributed denial of service (DDoS) attacks that flood services with traffic, causing downtime or performance issues. This research introduces a novel approach for sensing and preventing Cloud DDoS attacks by addressing the unique challenges of dynamic and distributed cloud environments. The novelty of this work lies in three main contributions: (i) Preprocessing, (ii) Feature extraction, (iii) Attack detection, and (iv) Attack mitigation. In the preprocessing phase, the input dataset is balanced using random undersampling and processed with an enhanced decimal scaling (EDS) normalization technique. The feature extraction phase involves retrieving enhanced correntropy‐based features, higher‐order statistical (HOS) features, and raw features. Attack detection is carried out using a novel deep learning (DL)‐based hybrid model named improved LinkNet‐PolyNet (ILPNet), which combines improved LinkNet (ILNet) and PolyNet. This approach offers improved accuracy over the conventional LinkNet model by addressing bottlenecks between the encoder and decoder blocks. Lastly, an enhanced entropy‐based mitigation approach is used to reduce detected attacks. The efficiency of this approach is evaluated through comparisons with existing methods using detailed performance, statistical, and ablation analyses. The ILPNet strategy demonstrated superior performance compared to conventional methods, achieving an accuracy of 0.960, a precision of 0.953, and an F ‐measure of 0.961.