A METHOD FOR ADAPTIVE ESTIMATION OF THE PROBABILITY OF INFORMATION LEAKAGE IN SPECIAL-PURPOSE NETWORKS FOR MACHINE LEARNING

Abstract

This article presents a formalized model of information leakage in special-purpose networks (SPN) and develops a method for adaptive estimation of the probability of information leakage in such networks. The relevance of the research is driven by the growing number of sophisticated targeted cyber threats, the use of multi-layered attacks and covert data transmission channels, as well as the increasing role of insider threats in modern information and communication systems. It is shown that traditional protection measures, focused primarily on perimeter security, signature-based detection, and static access policies, do not provide an adequate level of protection against information leakage in the context of dynamic changes in the operating environment of SPNs. This paper proposes a formalization of the SPN as a set of nodes, information transmission channels, and risk factors that underlie the occurrence of leaks. The developed method is based on the integration of network, host, and contextual security indicators, the normalization of features, the formation of a risk factor vector, the adaptive updating of weight coefficients, and the determination of an integral risk function, followed by the calculation of the probability of information leakage. A key feature of the method is its ability to be applied in real time, taking into account changes in user behavior, network operating modes, and the current threat level. An example of the practical implementation of the proposed approach is provided, demonstrating the sequence of processing security indicators, risk assessment, and decision-making regarding incident response. The results confirm that applying the proposed method improves the effectiveness of detecting precursors to information leaks, reduces the risk of compromising confidential data, and ensures that the protection system adapts to new types of threats in the context of targeted countermeasures.