In modern conditions, information flows are complicated by unreliable and harmful data, which negatively affects management systems and information security. Tools are needed to filter information before it is processed. The increase in false and malicious messages requires effective algorithms for analyzing and managing data that ensure the stability of automated systems. The purpose of the research is to create effective mathematical and computational methods for the analysis, classification and management of information to improve the reliability of systems and the reliability of data. A method of simulation modeling based on a mathematical model with elements of probability theory is proposed, where the information flow is divided into reliable, false and harmful information. To classify messages, probabilistic methods are used, taking into account prior and posteriori probabilities, as well as the analysis of network, temporal and semantic characteristics. Unlike existing methods, this one focuses on analyzing data before it is used, which reduces the risk of destructive impacts. A mathematical model has been developed for the analysis of information flows, including reliable, false and malicious information. The model uses probabilistic approaches and considers the network, temporal and semantic characteristics of messages to classify them and minimize their destructive impact. The model allows you to effectively consider the characteristics of each source, distinguishing reliable, false and malicious messages, which ensures high accuracy and reliability of the resulting information flow. This end-to-end solution improves data integrity and can be used in management and information security systems to minimize the impact of disruptive information and enable informed decision-making. The results can be used to monitor information threats, filter malicious information and ensure the security of critical systems, as well as support decision-making in government agencies, the economy and energy, increasing trust in information systems.
Nataliya Yartseva (Fri,) studied this question.