Table 10 Comparative analysis of HDLID-ECSOA model under ToN-IoT dataset21,35,36,37,38.
ToN-IoT Dataset | ||||
|---|---|---|---|---|
Approach | \(\:\varvec{A}\varvec{c}\varvec{c}{\varvec{u}}_{\varvec{y}}\) | \(\:\varvec{P}\varvec{r}\varvec{e}{\varvec{c}}_{\varvec{n}}\) | \(\:\varvec{R}\varvec{e}\varvec{c}{\varvec{a}}_{\varvec{l}}\) | \(\:{\varvec{F}}_{\varvec{M}\varvec{e}\varvec{a}\varvec{s}\varvec{u}\varvec{r}\varvec{e}}\) |
LSTM | 97.67 | 89.96 | 81.23 | 81.89 |
RF | 89.53 | 89.92 | 86.14 | 80.11 |
AdaBoost | 89.92 | 89.66 | 79.65 | 84.61 |
kNN Algorithm | 94.58 | 89.89 | 80.25 | 82.15 |
XGBoost | 91.95 | 89.75 | 81.21 | 78.61 |
CART Method | 95.89 | 89.30 | 83.58 | 80.63 |
1D CNN | 97.46 | 89.87 | 82.86 | 84.56 |
EPCOD | 90.15 | 90.57 | 86.90 | 80.88 |
DNN | 90.42 | 90.33 | 80.18 | 85.15 |
EEDOS | 95.29 | 90.41 | 80.83 | 82.87 |
HDLID-ECSOA | 99.33 | 91.37 | 87.07 | 88.54 |
