<p>The environment of cloud computing is severely harmed by security issues, which also harm the industry's long-term development. Intrusion detection is one strategy for averting damaging assaults on the cloud computing environment. Due to the enormous scale, dimensionality, and redundancy of network traffic in cloud computing environments, the development of cloud intrusion detection systems (IDS) is difficult. Thus, in this work, we create a hybrid feature selection method using an intrusion detection model based on a hybrid Hopfield recurrent neural network (H²RNN). Three stages include the developed model: pre-processing, feature selection, and classification. Before the pre-processing, the data are initially gathered from the dataset. The important features are chosen from each packet following pre-processing. A new hybrid feature selection model is created for feature selection. The hybrid model is a combination of support vector machine-recursive feature elimination (SV-RFE), minimum redundancy maximum Relevance (mRMR), and an enhanced coati optimization (ECO) algorithm. The H2RNN classifier is then given the chosen features to determine if a packet is normal or abnormal. NSL-KDD and KDD Cup 99are the two datasets used for experimental evaluation. The results demonstrate that the current method gives greater classification efficiency compared to four other state-of-the-art methods.</p>