Abstract

The increasing complexity and dynamism of cloud environments have introduced significant cybersecurity challenges, necessitating advanced risk assessment methodologies capable of handling uncertainty and evolving threats. Traditional risk management frameworks often rely on static and rule-based mechanisms, which lack adaptability in dynamic cloud ecosystems. To address these limitations, this chapter explores the integration of fuzzy logic and evolutionary computation for adaptive cyber risk management in cloud environments. Fuzzy logic provides a powerful framework for modeling imprecise security parameters and uncertainty in threat landscapes, enabling more flexible and context-aware risk assessment. Meanwhile, evolutionary computation offers an adaptive mechanism to optimize cybersecurity strategies through heuristic learning and intelligent decision-making. The chapter presents a hybrid risk assessment framework that leverages fuzzy inference systems to quantify risk levels and evolutionary algorithms to dynamically optimize security controls, it examines the scalability of fuzzy-evolutionary approaches in large-scale cloud infrastructures and their effectiveness in mitigating real-time cyber threats, such as zero-day attacks, insider threats, and advanced persistent threats. The potential integration of explainable AI (XAI), deep learning, and quantum computing in enhancing fuzzy-based risk assessment models is also discussed. This research contributes to the advancement of self-learning, adaptive cyber defense mechanisms capable of proactively mitigating risks in multi-cloud and hybrid-cloud environments. The proposed framework ensures improved threat intelligence, automated risk prioritization, and enhanced decision transparency, offering a robust solution for next-generation cloud security.  

Introduction

The rapid evolution of cloud computing has revolutionized modern digital infrastructures, offering on-demand scalability, cost efficiency, and enhanced computational power. However, the increasing adoption of cloud environments has also introduced significant cybersecurity risks, as dynamic cloud ecosystems are inherently vulnerable to data breaches, insider threats, and advanced cyberattacks. Traditional risk management frameworks, which rely on predefined security policies and static rule-based approaches, struggle to adapt to the ever-changing nature of cloud threats. The complexity of multi-cloud and hybrid-cloud environments further amplifies security challenges, as organizations must navigate heterogeneous infrastructures with varying security postures. Addressing these limitations requires an adaptive and intelligent cybersecurity framework capable of handling real-time risk assessment and threat mitigation. 

Fuzzy logic, a powerful mathematical approach for dealing with uncertainty and imprecise data, offers a promising solution for cyber risk assessment in cloud environments. Unlike binary decision-making models, fuzzy logic enables a gradual and context-aware evaluation of cyber risks, allowing for flexible and adaptive security assessments. By defining security parameters in linguistic terms rather than rigid numerical values, fuzzy logic provides an intuitive framework for quantifying risk severity, threat probabilities, and mitigation priorities. This adaptability is crucial in cloud security, where threat landscapes are constantly evolving, and conventional risk assessment models fail to capture uncertainty and incomplete information. Despite its advantages, standalone fuzzy logic models have limitations in optimization and computational efficiency, making it essential to integrate them with evolutionary computation techniques to enhance their adaptability and performance.