Table 12 Error analysis of HDLID-ECSOA method with existing models under ToN-IoT dataset.
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 | 2.33 | 10.04 | 18.77 | 18.11 |
RF | 10.47 | 10.08 | 13.86 | 19.89 |
AdaBoost | 10.08 | 10.34 | 20.35 | 15.39 |
kNN Algorithm | 5.42 | 10.11 | 19.75 | 17.85 |
XGBoost | 8.05 | 10.25 | 18.79 | 21.39 |
CART Method | 4.11 | 10.70 | 16.42 | 19.37 |
1D CNN | 2.54 | 10.13 | 17.14 | 15.44 |
EPCOD | 9.85 | 9.43 | 13.10 | 19.12 |
DNN | 9.58 | 9.67 | 19.82 | 14.85 |
EEDOS | 4.71 | 9.59 | 19.17 | 17.13 |
HDLID-ECSOA | 0.67 | 8.63 | 12.93 | 11.46 |
