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Journal of Big Data

Table 26 Mean performance of 7 classifiers in terms of AUC and F1-score on datasets with features from feature group 2A

From: Detecting cybersecurity attacks across different network features and learners

Classifier

Feature group 2A

AUC

SD AUC

F1

SD F1

CatBoost

0.89547

0.01241

0.86477

0.01188

LightGBM

0.95883

0.00157

0.94159

0.00127

Decision Tree

0.89876

0.00026

0.86111

0.00032

Logistic Regression

0.55609

0.00018

0.20412

0.00056

Naive Bayes

0.56993

0.00018

0.24736

0.00055

Random Forest

0.93240

0.02058

0.90543

0.02154

XGBoost

0.94174

0.00116

0.91419

0.00057

  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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