Study of Multi-Class Classification Algorithms’ Performance on Highly Imbalanced Network Intrusion Datasets

DOI10.15388/21-INFOR457zbMath1485.68213OpenAlexW3198395019MaRDI QIDQ5019887

Viktoras Bulavas, Virginijus Marcinkevičius, Jacek Rumiński

Publication date: 11 January 2022

Published in: Informatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.15388/21-infor457

zbMATH Keywords

multi-class classificationimbalanced learningnetwork intrusion detectionSMOTEbias and variance decomposition

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• Scikit
• AdaBoost.MH
• Python
• XGBoost
• Statsmodels
• Imbalanced-learn
• SMOTE

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