A Theoretical Framework for Evaluating Security Risks in Cloud, Edge and Fog Computing

Abstract

The spread of the distributed computing paradigms such as cloud, edge, and fog computing has made a difference in current information systems architecture. Although each of these technologies provides incredible scalability, low latency, and a greater level of computational efficiency, they also create intricate multi-layered security vulnerabilities that cross through heterogeneous infrastructure environments. The conventional security evaluation strategies that are mainly set to centralised computing paradigms are ineffective in dealing with the dynamic and distributed characteristics of these emerging paradigms. The shared ecosystem between the cloud datacenters, edge nodes, and the fog computing devices introduces attack surfaces that are significantly wider than traditional conceptions of what security measures can encompass, and thus theoretically requires the development of new frameworks to evaluate risks across all tiers of the system. The research paper brings in a cohesive theoretical framework of the systematic assessment of security risks in cloud, edge, and fog computing setups. The main goals include the construction of mathematical models to quantify multi-dimensional security threats, algorithmic solutions to real-time risk evaluation and uniform metric measures for comparative security analysis across heterogeneous distributed computer systems. In the methodology that is proposed to be used, the topologies of distributed computing will be represented using graph theoretical modelling, with Bayesian inference networks to account for probabilistic risk analysis, and machine learning algorithms to adaptively represent and search for threats. The framework combines time analysis on the risk dynamics, space analysis on the extent of danger, and hierarchical modelling on the multitier safety analysis. Next, an in-depth analysis is conducted based on practical deployment on all major cloud platforms, assessment of edge computing testbeds, and implementation of fog computing. The experimental validation results prove that, with diverse distributed computing environments, the framework identification performance is 97.3% precision, and the 94.8% identification performance of security vulnerabilities. The methodology raises the threat detection accuracy by 23 percent compared to the current methods and lowers the false positive rates by 31 per cent. Scalability Performance analysis demonstrates scale to networks with more than 10,000 heterogeneous nodes with real-time assessment capability, which can maintain response times of under a second. The theoretical framework presents a broad base of security risk assessment in the contemporary distributed computing paradigms. The practice allows the proactive security management process, allows the formalisation of the process of risk assessment, and informs the decision-making process regarding infrastructure security investment. The flexibility and scalability of the framework make it an invaluable support in the protection of next-generation distributed computing environments.