Design of an Efficient Cloud Security Model through Federated Learning, Blockchain, AI-Driven Policies, and Zero Trust Frameworks

Abstract

In the current era of cloud computing where most of the Organizations are shifting from local Infrastructure to Cloud network and hence Cloud security where most of sensitive data is stored is one of the key concerns for highly scalable and critical network deployments. As there is high increase in the use of cloud networks and computing because of simple Virtual machines and containers, the necessity for strong and stringent security measures to protect against complex cyber- attacks is very important than it was few years before.. Traditional cloud security models often grapple with limitations such as centralized data vulnerabilities, static security policies, and inadequate access control mechanisms. Cyber-attacks are getting complex and impacting organization with critical information and business loss. Dark Web attacks are more sophisticated and impacting tactically via various ways and mechanisms on cloud. The paper surveys the state-of-the-art in cloud network and infrastructure security models and essentially evaluates their performance based on various risks, threats, vulnerabilities and empirical dataset collection and samples. The paper discusses the advantages and disadvantages of each model and highlights their suitability for different types of attacks. To address these challenges, this research introduces a groundbreaking complex and stringent security techniques and security framework, synergizing Federated Learning, Blockchain technology, AI-Driven Security Policy Management, and Zero Trust Network Access (ZTNA) principles. The proposed model leverages Federated Learning to decentralize machine learning processes, thereby safeguarding data privacy and minimizing the risks associated with centralized data repositories. Concurrently, the integration of Blockchain technology ensures immutable and transparent transaction records, enhancing the integrity and trustworthiness of cloud interactions. Complementing these, AI-Driven Security Policy Management, employing algorithms like Reinforcement Learning and Decision Trees, automates the generation and implementation of dynamic security policies. This AI-based approach is adept at responding to evolving threats and adapting to changing network conditions in real-time scenarios. Furthermore, the adoption of Zero Trust principles, operationalized through Software-Defined Perimeter frameworks, enforces a stringent 'never trust, always verify' approach. This paradigm shift is critical in fortifying access controls, effectively mitigating the risks of unauthorized access and insider threats. The interplay of these technologies culminates in a robust, resilient cloud security architecture sets. Empirical evaluation in varied cloud scenarios showcases notable enhancements in security metrics. The integrated model outperforms existing methods, achieving a 3.5% increase in precision, 4.9% in accuracy, 2.4% in recall, 3.5% in Area Under the Curve (AUC), and 1.9% in specificity, alongside a 4.5% reduction in response delay. These improvements signal a significant leap in cloud network security, offering a comprehensive solution to contemporary cyber threats. The impact of this work is profound, paving the way for more secure, reliable, and efficient cloud computing environments.