Optimized deep learning methodology for intruder behavior detection and classification in cloud

Apps, networks, frameworks, and services are all made possible by the cloud. In order to get the most out of the improved accessibility and computational capabilities, the Service Providers can offer an optimal use of current services. Application services have been revolutionised as their launch since they are useful and cost-effective for both suppliers and users. Increasingly, cyber defence is a vital research area in today's environment, where networks are essential. The software and hardware of a network are constantly monitored by an intrusion detection framework (IDF), which is an essential part of any cyber defence plan. Many of the current IDSs are still struggling to improve detection performance, reduce false alarm rates and detect new threats. Based on parametric computation analysis, this study provides a deep learning approach to optimize cloud networks and to identify intruders. Results of suggested approach have been displayed from data compared to current methods in the conclusion section.