Application of Probabilistic Principles for Modeling Cyber Threats

Abstract

Applying probabilistic principles for modelling cyber threats represents a pivotal advancement in cybersecurity, offering innovative solutions to address the complexities and uncertainties inherent in today's threat landscape. This paper explores past research works, current trends, and future directions in probabilistic modelling for cyber threat analysis. Past research has demonstrated the effectiveness of probabilistic techniques such as Bayesian networks, Markov models, and Monte Carlo simulations for assessing cyber risks. These approaches enable organizations to quantify cyber threats' likelihood and potential impact by incorporating probabilistic models of threat actor behaviour, system vulnerabilities, and environmental factors. However, there remains a need to explore the integration of machine learning, artificial intelligence, blockchain, and collaborative threat intelligence sharing further to enhance the capabilities of probabilistic modelling in cybersecurity. Future directions for research and development in this area include the development of dynamic and adaptive risk assessment frameworks, integrating behavioural analytics and human factors into probabilistic models, and exploring blockchain and cryptographic solutions for securing decentralized networks. Additionally, collaborative and federated threat intelligence sharing platforms enabled by probabilistic modelling will facilitate greater information sharing and coordination among organizations, helping more proactive and coordinated responses to cyber threats.