Table 6 Comparison of classification rates with existing approaches in the literature on UNSW-NB15 data-set
References | Data used | Technique | Imputation | Classification accuracy (%) | ||||
|---|---|---|---|---|---|---|---|---|
Binary | Multi-class | Multi-class clusters | ||||||
Flow features | Transport features | Top features | ||||||
[8] | 10% | CNN-1D | – | 89.80 | 78.20 | – | – | – |
RF | 87.90 | 73.20 | – | – | – | |||
SVM (Linear) | 84.60 | 65.20 | - | – | – | |||
MLP | 86.60 | 74.90 | – | – | – | |||
[9] | 30% | DT | – | – | – | 95.32 (DNS) | 97.13 (HTTP) | - |
NB | – | – | 91.17 (DNS) | 95.91 (HTTP) | – | |||
ANN | – | – | 92.61 (DNS) | 96.27 (HTTP) | – | |||
Ensemble | – | – | 99.54 (DNS) | 98.97 (HTTP) | – | |||
[10] | 10% | GBT | – | 93.13 | – | – | – | – |
KNN | 91.90 | – | – | – | – | |||
DT | 92.29 | – | – | – | – | |||
LR | 92.35 | – | – | – | – | |||
NB | 92.52 | – | – | – | – | |||
SVM | 92.32 | – | – | – | – | |||
[11] | 10% | DT | – | 89.86 (22 Features) | – | – | – | – |
Proposed approach | 60% | RF | Mean, multiple and linear regression | 98.67 | 97.37 | 96.96 | 91.40 | 97.54 |
SVM | Â | 97.69 | 95.67 | 89.78 | 82.96 | 89.93 | ||
ANN | Â | 94.78 | 91.67 | 86.37 | 81.63 | 87.68 | ||