Table 3 Comparative outcome of SAMFL-SCDCOA model on CICIDS-2017 dataset24,25,34,35,36.
Model | \(Acc{u}_{y}\) | \(Pre{c}_{n}\) | \(Rec{a}_{l}\) | \({F}_{score}\) |
|---|---|---|---|---|
RF | 94.62 | 94.77 | 94.87 | 89.51 |
DT | 98.62 | 91.40 | 90.72 | 90.52 |
KNN algorithm | 94.53 | 93.86 | 93.64 | 96.09 |
AdaBoost | 98.58 | 92.41 | 94.30 | 93.09 |
DBN-KELM | 93.89 | 92.53 | 94.39 | 93.04 |
RNN method | 90.37 | 91.74 | 89.41 | 96.13 |
Gradient boosting | 90.06 | 97.04 | 92.82 | 92.05 |
DBRF | 94.59 | 93.93 | 93.71 | 96.16 |
FLIDS | 98.66 | 92.46 | 94.36 | 93.14 |
SMOTE-ENN | 93.95 | 92.59 | 94.46 | 93.11 |
SAMFL-SCDCOA | 99.14 | 97.68 | 97.85 | 97.76 |
