Abstract

Cloud computing has transformed modern IT infrastructure by providing scalable, flexible, and cost-efficient solutions. However, cloud environments are increasingly targeted by sophisticated cyber threats, making security a critical concern. Probabilistic threat modeling techniques provide a systematic approach to identify, quantify, and mitigate security risks in cloud infrastructures by incorporating uncertainty and likelihood into threat assessment. This paper explores various probabilistic methods, including Bayesian networks, attack graphs, Markov models, and stochastic Petri nets, applied to cloud security. These techniques allow organizations to evaluate the probability of successful attacks, prioritize high-risk components, and optimize resource allocation for mitigation strategies. The study highlights how probabilistic models capture dynamic cloud behaviors, interdependencies between components, and multi-stage attack scenarios more effectively than deterministic models. Evaluation metrics, such as risk probability, expected loss, and model accuracy, are discussed to validate the effectiveness of these models. The advantages, limitations, and practical applications of probabilistic threat modeling are analyzed to provide guidance for security analysts and cloud administrators. The findings underscore the potential of probabilistic techniques to enhance cloud infrastructure resilience, improve proactive threat management, and support informed security decision-making