METHOD FOR PREDICTING NODE COMPROMISE IN EDGE AND FOG ENVIRONMENTS FOR CRITICAL INFRASTRUCTURE

Abstract

The paper proposes a method for predicting node compromise in edge and fog environments for critical infrastructure systems. The relevance of the study is determined by the increasing number of cyberattacks on distributed IoT networks, where disruption of individual components may lead to data integrity violations, reduced node reliability, and degradation of information infrastructure operation. The proposed method is based on the analysis of behavioral and network characteristics of edge/fog components, feature vector formation, anomaly score calculation, integrated compromise risk assessment, trust level estimation, and the use of a
generalized danger indicator. A distinctive feature of the approach is the combination of anomaly detection with state prediction, which makes it possible not only to identify current deviations in node behavior but also to estimate the probability of their transition to a potentially dangerous state. The use of machine learning and neural network elements provides the basis for adapting the method to dynamic operating conditions of distributed systems. The proposed approach can be used to
improve data protection, support timely detection of anomalous behavior, and implement proactive IoT network security mechanisms in critical infrastructure systems.