Omoyemi Abimbola Oni

An Improved Stochastic Model for Cybersecurity Risk Assessment" introduces a novel approach to evaluating and predicting cybersecurity risks within network systems. Traditional methods often focus on identifying threats and vulnerabilities but may lack effective capabilities to counteract risks and perform long-term predictions. To address this gap, the authors propose a hybrid model that combines the Absorbing Markov Chain and Markov Reward Model to assess the cybersecurity state of a selected network. The methodology involves scanning and analyzing real-time vulnerabilities, evaluating the ease of exploitability, and formulating a risk assessment model based on the combined Markov models. Simulation results using the R statistical package demonstrate that the proposed model offers higher reliability and availability compared to existing models, suggesting significant improvements in assessing security situations in cyberspace networks.