Work

Method for Determining the Optimal Hyperparameters of Gradient Boosting Models for Classifying the Processes of Functioning of Energy-dependent Memory of the Organization's Information System Assets

Year: 2025

Type: conference-paper

Abstract: Detecting anomalous activity in the energy-dependent memory of an organization's information system assets is an actual task to ensure their protection. Gradient Boosting methods [1], such as HistGradientBoosting, LightGBM, XGBoost, and AdaBoost, demonstrate high accuracy in classification tasks, but their performance significantly depends on the configuration of hyperparameters [2-5].

DOI: 10.36074/logos-09.05.2025.067

Authors: Mariia Hanchenko, Serhii Gakhov

Institution: State University of Information and Communication Technologies

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

OpenAlex ID: W4411765787

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