Table 5 Comparative outcomes of MOBCF-ADDLM technique with existing approaches19,36]– [37.
Technique | \(\:\varvec{A}\varvec{c}\varvec{c}{\varvec{u}}_{\varvec{y}}\) | \(\:\varvec{P}\varvec{r}\varvec{e}{\varvec{c}}_{\varvec{n}}\) | \(\:\varvec{R}\varvec{e}\varvec{c}{\varvec{a}}_{\varvec{l}}\) | \(\:{\varvec{F}}_{\varvec{s}\varvec{c}\varvec{o}\varvec{r}\varvec{e}}\) |
|---|---|---|---|---|
MOBCF-ADDLM | 99.22 | 98.38 | 95.83 | 96.98 |
LR | 98.72 | 96.67 | 93.98 | 95.71 |
XGBoost | 97.87 | 94.96 | 92.90 | 95.83 |
HGBClassifier | 97.76 | 95.66 | 94.36 | 95.34 |
H3SC-DLIDS | 99.07 | 96.68 | 95.20 | 96.06 |
AE-MLP Method | 98.21 | 95.93 | 93.34 | 95.15 |
XGBoost Method | 97.12 | 94.31 | 92.15 | 95.08 |
RF | 97.02 | 95.00 | 93.72 | 94.59 |
DT | 95.24 | 92.46 | 92.54 | 93.29 |
Bi-LSTM | 97.43 | 95.83 | 94.93 | 95.55 |
Hybrid IDS | 96.92 | 94.80 | 90.26 | 92.89 |
