Review of Mamdani Fuzzy Inference System for Modeling Cyber Threats

Abstract

This review paper explores the application of Mamdani Fuzzy Inference Systems (FIS) in modeling cyber threats, highlighting their role in enhancing threat detection and management in complex cybersecurity environments. Mamdani Fuzzy Inference Systems, known for their intuitive approach to handling uncertainty and imprecision, offer valuable tools for interpreting and managing the dynamic nature of cyber threats. This paper provides an overview of the fundamental concepts of fuzzy logic and the structure of Mamdani Fuzzy Inference Systems, including fuzzification, inference, and defuzzification processes. It examines various applications of Mamdani Fuzzy Inference Systems in cyber threat modeling, such as threat classification, risk assessment, and management, supported by case studies and practical examples. The review also compares Mamdani Fuzzy Inference Systems with other fuzzy and non-fuzzy approaches, discussing their advantages and limitations. Challenges such as computational complexity, data quality, and integration with existing systems are addressed alongside future research opportunities. By synthesizing current knowledge and identifying potential advancements, this paper aims to provide a comprehensive understanding of how Mamdani Fuzzy Inference Systems can be effectively utilized to address the evolving landscape of cyber threats.