Cycle‐consistent generative adversarial network optimized with water strider optimization algorithm fostered intrusion detection framework for securing cloud computing environment

Summary Cloud computing is the delivery of computing services including servers, storage, databases, networking, software, analytics, and intelligence over the internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale. In this article, cycle‐consistent generative adversarial network (CCGAN) optimized with water strider optimization (WSO) algorithm fostered intrusion detection system (IDS) is proposed to secure the cloud computing (CC) environment (IDS‐CC‐CCGAN‐WSOA). Initially, the input data's are gathered via NSL‐KDD benchmark dataset. Then it is given to preprocessing. During preprocessing, it debugs the redundancy and missing value is restored using local least squares. Then, the preprocessed output is fed to the feature selection level. The optimum features are compiled utilizing correlation feature selection approach. This optimum features based, the data's are categorized as normal and anomalous data. The weights of this network are optimized by water strider optimization to attain effectual and optimum solution for recognizing the intruders. The proposed approach is executed in MATLAB. The performance metrics is examined to validate the performance of the proposed approach. Finally, the proposed approach attains 13.9367%, 13.268%, and 13.739% higher accuracy analyzed to the existing approaches, such as IDS‐CC‐DBN‐CSSA, IDS‐CC‐DNN‐IGASAA, and IDS‐CC‐MLPNN‐ABC.