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

¹ Department of Computer Science Engineering, Hyderabad Institute of Technology and Management, India
² PS Consulting and Solutions
³ Department of Computer Science and Engineering, S.A. Engineering College, Poonamallee-Avadi Road, Thiruverkadu, Chennai, Tamil Nadu, India, 600077
⁴ Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, India, 603203
⁵ Computer Science Engineer, Oregon State University, Corvallis, Oregon, USA 97331
⁶ Rajeev Institute of Technology, Hassan, India
⁷ Department of CSE, Gitam Deemed to be University, Hyderabad, Rudararam, Sangareddy District, Telangana, India, 502329

^* Corresponding author: scarvi@rediffmail.com

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.