COMPARATIVE ANALYSIS OF MODELING METHODS AND TECHNOLOGIES IN CYBERSECURITY

Abstract

The cybersecurity landscape is characterized by high complexity and dynamism, necessitating advanced modeling methods for threat analysis, risk prediction, and evaluation of protective measures. This article presents a detailed comparative analysis of traditional and contemporary modeling approaches in cybersecurity, including mathematical, logical, and hierarchical modeling, attack simulations and Breach and Attack Simulation (BAS), agent-based modeling, digital twins, as well as methods based on machine learning, deep learning, game theory, graph structures, and large language models (LLMs). Each method is examined in terms of its operational principles, key advantages, limitations, and practical applications. Particular attention is given to the synergy and complementarity of these approaches, which are critical for developing comprehensive and adaptive cybersecurity systems. Traditional methods, such as mathematical modeling, provide a formal basis for analysis but may oversimplify real-world scenarios. Contemporary approaches, including machine learning and digital twins, enable the processing of large data volumes and modeling of complex dynamic interactions, though they require significant computational resources and accurate data. Game theory and graph models offer strategic and contextual analysis, while large language models open new possibilities for automating threat analysis, despite their reliability limitations. The integration of these methods forms the foundation for hybrid solutions that mitigate the shortcomings of individual approaches, enhancing overall protection efficacy. The article also highlights challenges related to computational complexity, uncertainty, and ethical considerations, and outlines future directions, such as improving explainable AI, resilience to adversarial attacks, and simulation realism.