INTELLIGENT INFORMATION SYSTEM FOR ADAPTIVE ANOMALIES DETECTION IN DISTRIBUTED ENVIRONMENTS BASED ON HYBRID ARTIFICIAL INTELLIGENCE MODELS

Abstract

The article considers the problem of anomaly detection in distributed information environments operating under
dynamic loads, high intensity of information flows, data heterogeneity and the growth of cyber threats. The relevance of
the study is due to the development of cloud technologies, the Internet of Things (IoT) and big data processing systems,
which complicates the monitoring of the state of information systems and requires the use of intelligent analysis methods.
It has been established that traditional approaches based on static rules and threshold values do not provide sufficient
accuracy and adaptability, which leads to false positives or missing critical events. The purpose of the study is to develop
an intelligent information system for adaptive anomaly detection based on hybrid artificial intelligence models. To
achieve this, neural networks, machine learning methods and fuzzy logic were used. A system architecture with data
collection, forecasting, anomaly detection and decision-making modules is proposed. A mathematical model for assessing
anomalies based on the deviation between actual and predicted parameters, as well as an integral indicator taking into
account vulnerability and criticality, has been developed. A fuzzy logic inference mechanism has been implemented to
form control influences. Simulation modeling has been conducted for various scenarios, which confirmed an increase in
the accuracy of anomaly detection, a decrease in false positives, and a reduction in the system response time.
Keywords: intelligent information system; cloud technologies; distributed systems; anomaly detection; machine
learning; neural networks; fuzzy logic; adaptive systems; artificial intelligence.

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