The tremendous growth of internet technology has drastically improved the large amount of connected devices. To secure network infrastructure from the damage that cyberattacks might cause, this has made an enormous attack surface that needs the deployment of practical and effective countermeasures. In the contemporary era of active network transmission and throughput, Intrusion Detection System (IDS) plays a vital role in ensuring secure network resource and data from outside invasion. In recent times, IDS becomes an essential tool to enhance the efficiency and flexibility for unpredictable and unexpected invasions of the network. Deep learning (DL) is a well-known and essential method to resolve challenges and could learn rich features of massive information. Therefore, the study focuses on the design and development of the Cauchy Grasshopper Optimization Algorithm with Deep Learning for Cloud Enabled IDS (CGOA-DLCIDS) technique. The presented CGOA-DLCIDS method aims to recognize the presence of intrusions in the cloud platform. To achieve this, the CGOA-DLCIDS technique performs feature subset election by CGOA which reduces the feature subset and enhances the intrusion detection rate. Next, the CGOA-DLCIDS technique employs attention based long short-term memory (ALSTM) module for automated and accurate intrusion detection and classification. The simulations analysis of the CGOA-DLCIDS method on benchmark dataset highlighted the increasing results compared to recent IDS approaches.