Optimum-path forest stacking-based ensemble for intrusion detection
Carregando...
Arquivos
Fonte externa
Fonte externa
Data
Orientador
Coorientador
Pós-graduação
Curso de graduação
Título da Revista
ISSN da Revista
Título de Volume
Editor
Springer
Tipo
Artigo
Direito de acesso
Arquivos
Fonte externa
Fonte externa
Resumo
Machine learning techniques have been extensively researched in the last years, mainly due to their effectiveness when dealing with recognition or classification applications. Typically, one can comprehend using a Machine Learning system to autonomously delegate routines, save human efforts, and produce great insights regarding decision-making tasks. This paper introduces and validates a stacking-based ensemble approach using Optimum-Path Forest classifiers in intrusion detection tasks. Instead of only using the famous NSL-KDD dataset, we propose a new dataset called uneSPY, which we believe will fill the gap concerning new intrusion detection datasets. Both datasets were evaluated under several classifiers, including Logistic Regression, Decision Trees, Support Vector Machines, Optimum-Path Forests, and compared against Optimum-Path Forest stacking-based ensembles. Experimental results showed an Optimum-Path Forest stacking-based ensemble classification suitability, particularly when considering its ability to generalize large volumes of data while sustaining its performance.
Descrição
Palavras-chave
Machine learning, Optimum-path forest, Ensemble, Stacking-based ensemble, Intrusion detection
Idioma
Inglês
Citação
Evolutionary Intelligence. Heidelberg: Springer Heidelberg, 18 p., 2021.
