An Advanced Dynamic Trust Management and Security Configuration Technology For Hybrid Cloud Mesh Architecture

Abstract

In the modern era, cloud computing (CC) is more important as corporations increasingly adopt hybrid cloud infrastructure to enhance scalability and flexibility. Here, the hybrid cloud setup integrates one or more external public cloud systems to execute the workloads securely within the internal system. In some cases, cloud providers have missed the different security policies that can manage several cloud environments. This may be a reason for decreased performance under latency and data transfer ratios. These cloud environments fail to ensure safe and secure trustworthy communication between the decentralized distributed services. However, the traditional strategies are slowly securing the data seekers from the cloud networks. Therefore, this article introduces a novel framework to address the challenges faced in cloud infrastructure. Consequently, the proposed framework is named Reinforcement Learning-based Zero Trust with Trust Score (RLZT-TS) architecture. This integrates an RL algorithm, a dynamic trust scoring (DTS) model, and a Zero Trust architecture (ZTA) in a hybrid cloud mesh infrastructure. This model can effectively detect cyber threats. Also, for security purposes, an Advanced Multi-Factor Authentication (AMFA) protocol has been implemented in the security layer to secure the data during transmission over the cloud networks. The novel feature of RLZT-TS is a closed-loop adaptive trust model, where the trust score is continuously updated using RL algorithms, the access policy is dynamically adjusted based on the trust score, and the authentication requirements are dynamically changed based on the predicted risk level. The proposed RLZT-TS paradigm can support automated security reconfiguration and trust assessment towards the zero-trust values. Consequently, the model ensures intelligent security replica against evolving cybersecurity threats. Consequently, the developed framework is analysed through the MATLAB simulation, and outcomes illustrate that the significant enhancement in trust score accuracy (98%), attack detection rate (99%), resource utilization (8.82%), policy adaptability (98%), and latency (8.2ms).