КОМПЛЕКСНИЙ ПІДХІД ДО ІНТЕЛЕКТУАЛЬНОГО УПРАВЛІННЯ, МОДЕЛЮВАННЯ ТА ВИЯВЛЕННЯ МЕРЕЖНИХ АНОМАЛІЙ НА ОСНОВІ ЕНТРОПІЙНИХ ТА НЕЙРОМЕРЕЖЕВИХ ПІДХОДІВ

DOI 10.31673/2412-4338.2025.025703

Abstract

Abstract. This article presents a methodology for evaluating the reliability of information systems using systems analysis and entropy-based approaches. The proposed method is grounded in the formalization of diagnostic markers classified into three types: behavioral, structural, and performance-related. Within the study, an entropy-based reliability metric, RH, was developed to quantify the uncertainty level of a system’s state in realtime. Implementation variants using sliding windows and fixed intervals are proposed, providing high accuracy in failure prediction. Experimental modeling was conducted using data generated in the NetSim environment for a 200-node network. It was found that when RH drops below 0.6, the probability of failure within the next 30 minutes exceeds 85%. Additionally, the study examines correlations between different categories of markers and finds that combinations of structural and behavioral signals have the highest predictive potential. Heatmaps and histograms were generated to visualize the results, enabling integration of the model into decision support systems. Special attention was given to the construction of temporal activity profiles of network components and their impact on entropy indicators. The paper also evaluates the adaptability of the model to highly dynamic environments, such as cloud and distributed IoT architectures. Algorithmic approaches to automatic recalibration of system parameters under changing configurations or workloads are proposed. The research results demonstrate the high effectiveness of combining entropy analysis with marker-based approaches to enable proactive monitoring, early failure detection, and enhancement of cyber-resilience in complex information systems operating in unstable digital environments. The potential application of the proposed model is considered for industrial control systems, banking IT infrastructures, critical digital security facilities, and integration with neural network technologies.

Keywords: information systems reliability, entropy assessment, diagnostic markers, failure prediction, RH metric, heatmaps, sliding window, structural faults, NetSim, automatic response.