Research Article
A Residual Learning-Based Network Intrusion Detection System
Table 6
Comparison with other detection methods on UNSW-NB15 testing set.
| Model | Testing metrics | TN | TP | Acc | RW |
| RLF-CNN | 36758 | 36278 | 0.887 | 0.893 | RLC-CCNN | 36996 | 35429 | 0.879 | 0.881 | SMOTE-RF [32] | 36952 | 32286 | 0.841 | 0.826 | Pelican [19] | 36850 | 34928 | 0.872 | 0.859 | S-ResNet [1] | 36928 | 31427 | 0.830 | 0.842 |
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