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Journal of Big Data

Table 27 Mean performance of 7 classifiers in terms of AUC and F1-score on datasets with features from feature group 3A

From: Detecting cybersecurity attacks across different network features and learners

Classifier

Feature group 3A

AUC

SD AUC

F1

SD F1

CatBoost

0.88616

0.00304

0.85593

0.00363

LightGBM

0.95832

0.00088

0.93869

0.00152

Decision Tree

0.88518

0.01677

0.83763

0.01621

Logistic Regression

0.55352

0.00021

0.19546

0.00066

Naive Bayes

0.56753

0.00287

0.24000

0.00887

Random Forest

0.93496

0.01585

0.90416

0.01719

XGBoost

0.94780

0.00020

0.91189

0.00031

  1. Best metrics are highlighted in italics; SD AUC is the standard deviation of AUC and SD F1 is the standard deviation of the F1-score
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