Using bayesian models and Monte Carlo methods to predict cyber threats

The methods for forecasting cyber threats and risk analysis using Bayesian models and Monte Carlo methods are considered in the article. Bayesian models have the capability to account for conditional probabilities and dynamically update estimates based on incoming data, ensuring a high degree of flexibility and adaptability in threat forecasting, particularly in conditions of uncertainty and a rapidly changing cyber environment. These models enable the consideration of the interrelationships between various factors and events, significantly enhancing the accuracy of predictions. Monte Carlo methods, through multiple simulations and scenario analysis, allow for a detailed assessment of risks and the likelihood of various events. The example of a Bayesian model structure that includes key elements of cybersecurity such as firewalls, malware, data breaches, social engineering, cloud services, and external networks is presented. The results of Monte Carlo simulations reveal strong correlations between these elements. For instance, reduced firewall effectiveness increases the likelihood of malware infiltration, which, in turn, significantly raises the risk of data breaches. The success of social engineering attacks also greatly impacts the likelihood of data breaches. These identified interdependencies help in developing more precise and effective cybersecurity strategies by focusing efforts on critical nodes and potential vulnerability points. Such an approach enables cognitive security centers and cybersecurity experts to forecast threats, analyze risks, devise proactive defense measures, and make informed decisions aimed at enhancing the protection of critical infrastructure.