ADAPTIVE MODEL FOR ANOMALY DETECTION IN NETWORK TRAFFIC USING MACHINE LEARNING METHODS

The article is dedicated to the development of an intelligent system for detecting anomalies in network traffic using machine learning methods. It examines in detail the relevance of this problem for ensuring cybersecurity, analyzes the shortcomings of existing manual approaches, and justifies the need for automated solutions. The article describes the architecture of the developed system, including components for traffic capture, data preprocessing, model training based on the One-Class SVM algorithm, and automatic adaptation to network changes. Special attention is paid to the process of anomaly detection, model quality assessment, and mechanisms for monitoring and alerting about detected incidents.