Table 5 CT analysis of IHDLM-CADEFST technique with existing models under ToN-IoT and Edge-IIoT datasets.
Dataset | Approach | CT (s) |
---|---|---|
ToN-IoT dataset | dAE | 13.70 |
HDBSCAN | 8.39 | |
CALR | 10.94 | |
DNN | 6.74 | |
CART | 12.26 | |
XGBoost | 8.87 | |
CNN-RNN | 10.54 | |
RepuTE algorithm | 12.58 | |
Neural network | 14.23 | |
SVM | 11.91 | |
IHDLM-CADEFST | 3.29 | |
Edge-IIoT dataset | EDLM-PSOFS | 12.56 |
GA-LSTM | 11.91 | |
EfficientNetB0 | 26.79 | |
Random forest | 21.23 | |
KNN | 19.36 | |
SVM | 11.90 | |
XGBoost | 24.79 | |
LightGBM | 12.95 | |
TabPFN | 26.84 | |
Voting classifier | 10.26 | |
IHDLM-CADEFST | 6.82 |