Adaptive Model for Anomaly Detection in Network Traffic Using Machine Learning Methods
Year: 2024
Type: article
Abstract: 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.
Authors: Kerimov, Komil Fikratovich, Kurbanov, Sardor Nuriddinovich, Azizova, Zarina Ildarovna
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6967658696
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