A Deep Learning Approach to Secure and Efficient Cloud Resource Deployment

Abstract

Cloud computing settings face growing difficulties in efficient resource use, secure data processing, and proactive detection of threats due to their dynamic and massive nature. Placing and moving virtual machines (VMs) in incorrect locations can waste resources, slow down performance, and raise operating expenses. The present research presents a deep learning--driven secure cloud resource deployment architecture that includes intelligent VM placement and migration, adaptive encryption, and machine learning--based anomaly detection to solve these problems. The suggested approach uses deep learning models to make more accurate decisions on where to place and move virtual machines by looking at real-time workload patterns, resource availability, and system performance metrics. This method uses less energy, lowers latency, and makes better use of resources in all cloud data centers. To keep data secure, adaptive encryption methods based on powerful cryptographic protocols are used dynamically dependent on how sensitive the data is and how it can be accessed. It allows for real-time data protection with minimal extra processing power needed. Additionally, the framework includes machine learning-based models for finding unusual activity in the cloud that are continually functioning. These models look at network traffic, user behaviours, and system logs to find strange patterns and possible security holes. When a vulnerability is found, automated threat response systems are activated to lower risks while maintaining services functioning. Evaluations show that the proposed framework makes VM deployment more efficient, makes data security stronger, and makes anomaly detection more accurate than standard cloud management methods.