Table 20 Maximum AUC by classifier for the data exfiltration 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 RUS 1:1 7 Agree | 0.96968 | 0.02346 |
CatBoost Tuned RUS 1:9 4 Agree | 0.99076 | 0.01461 |
DT RUS 1:3 4 Agree | 0.98398 | 0.01531 |
Light GBM Tuned RUS 1:3 5 Agree | 0.98947 | 0.01328 |
RF RUS 1:1 6 Agree | 0.99024 | 0.01120 |
MLP RUS 1:3 4 Agree | 0.96914 | 0.02814 |
NB Tuned RUS 1:3 7 Agree | 0.97958 | 0.01523 |
XGBoost RUS 1:3 6 Agree | 0.98737 | 0.01497 |