Table 5 Maximum AUC by classifier for the information theft attack type; mean and standard deviations of AUC, (10 iterations of 5-fold cross-validation)
From: IoT information theft prediction using ensemble feature selection
Experiment name | Mean | SD |
|---|---|---|
AUC | AUC | |
LR Tuned 7 Agree | 0.99504 | 0.00218 |
CatBoost RUS 1:3 5 Agree | 0.99838 | 0.00131 |
DT 4 Agree | 0.99589 | 0.00199 |
Light GBM RUS 1:1 6 Agree | 0.99757 | 0.00192 |
RF RUS 1:1 4 Agree | 0.99732 | 0.00182 |
MLP Tuned 6 Agree | 0.99537 | 0.00242 |
NB 7 Agree | 0.99324 | 0.00203 |
XGBoost RUS 1:1 4 Agree | 0.99731 | 0.00169 |