Utilizing deep learning and optimization methods to enhance the security of large datasets in cloud computing environments

Abstract

Many firms are outsourcing their information and computational needs because of the fast advancement of modern computing technology. Cloud-based computing systems must provide safeguards, including privacy, accessibility, and integrity, making a highly reliable platform crucial. Monitoring malware behavior throughout the whole characteristic spectrum significantly enhances security tactics compared to old methods. This research offers a novel method to improve the capacity of Cloud service suppliers to analyze users' behaviors. This research used a Particle Swarm Optimization-based Deep Learning Model the identification and optimization procedure. During recognition procedure, the system transformed users' behaviors into an understandable format and identified dangerous behaviors using multi-layer neural networks. The analysis of the experimental data indicates that the suggested approach is favorable for use in security surveillance and identification of hostile activities.