The article is devoted to the method of formalizing indicators of compromise (IoC) using a Bayesian approach to classify and rank them based on probabilistic inference. The problem of detecting malicious indicators from a large volume of data found in various sources of threat information is critically important for assessing modern cybersecurity systems. Traditional heuristic approaches, based on simple aggregation or expert evaluation of IoCs, do not provide sufficient formalization and further ranking of their reliability regarding their association with a particular malicious campaign due to the incompleteness and uncertainty of the information received from various sources.

Keywords: indicators of compromise (IoC), Bayesian inference, cyber threats, probabilistic models, malicious activity analysis, threat intelligence, IoC classification, multi-source analysis

The coefficients of determination and the absolute values of forecast assessment results based on the use of linear trends for different samples of initial data, varied by increasing amplitude over time intervals, are considered. A new linear method of forecast boundaries for forecast assessment (data extrapolation) is proposed.

Keywords: system analysis, statistical data, mathematical trend assessment, forecast evaluation, confidence interval, method of forecast limits