Integrated Framework for Data Security in Cloud Computing Using Deep Learning Techniques

Abstract

Alongside the expansion of the digital economy, data centers have grown substantially in size and quantity. Data centers are becoming increasingly essential to the development of the economy and society. However, even a brief outage in a data center might have severely negative effects. Resolving this issue requires secure management of data centers' physical infrastructure. Defenses against different cyber threats are being developed for the Internet of Things (IoT) and Cyber Physical Systems (CPS). As malicious code becomes more prevalent, using cloud environments to find dangerous code might not be a viable approach in the future. Due to the growing inefficiency of traditional perimeter-based security models in today’s cloud-centric and remote work contexts, we employed integrated deep learning techniques for cloud data security in this article. According on risk profiles and real-time behavior, the suggested Zero-Trust security framework continuously evaluates and modifies the trust levels for users, devices, and applications. Using an integrated framework, we employed User and Entity Behavior Analytics (UEBA), Risk Scoring, and Adaptive Authentication approaches. This enabled us to reach an accuracy of 85–90% in all areas of data security when compared to the conventional methods.