Network Intrusion Detection on NSL-KDD
99.1AccuracyDeep NN
Evaluation results
| Method | Links | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Deep NN2026.06 | 99.1 | — | — | — | — | — | — | — | — | — | — | — | — | |
| CNN2026.02 | 99 | 88 | 84 | 86 | 99 | 99 | 98 | — | — | — | — | — | — | |
| LSTM2026.02 | 99 | 99 | 89 | 93 | 99 | 99 | 99 | — | — | — | — | — | — | |
| Popoola et al.Rounds/Epochs=92-100 rounds2026.06 | 95 | — | — | — | — | — | — | — | — | 92 | — | — | — | |
| Popoola et al.Rounds/Epochs=92–100 rounds2026.06 | 95 | — | — | — | — | — | — | — | — | 92 | — | — | — | |
| AdaptiveNADInference Paradigm=single-sample2024.10 | 87.56 | — | — | — | — | — | — | 89.64 | 87.27 | 87.2 | 0.71 | 24.74 | 87.27 | |
| multilayer defense mechanism2026.03 | 87.35 | — | — | — | — | — | — | — | — | — | — | — | — | |
| GDNInference Paradigm=single-sample2024.10 | 86.33 | — | — | — | — | — | — | 87.03 | 86.51 | 86.24 | 18.82 | 8.16 | 86.51 | |
| DeepAIDInference Paradigm=single-sample2024.10 | 85.97 | — | — | — | — | — | — | 86.95 | 86.25 | 85.93 | 22.03 | 5.45 | 86.25 | |
| Autoencoder2026.03 | 84.21 | — | — | — | — | — | — | — | — | — | — | — | — | |
| BAT-MC2026.03 | 84.15 | — | — | — | — | — | — | — | — | — | — | — | — | |
| AOC-IDSInference Paradigm=single-sample2024.10 | 83.82 | — | — | — | — | — | — | 86.8 | 84.13 | 83.43 | 29.2 | 2.54 | 84.13 | |
| DAGMMInference Paradigm=single-sample2024.10 | 79.92 | — | — | — | — | — | — | 80.88 | 79.61 | 79.62 | 10.11 | 29.72 | 76.6 | |
| AE-LOFInference Paradigm=single-sample2024.10 | 51.59 | — | — | — | — | — | — | 51.53 | 50.6 | 40.9 | 8.15 | 90.66 | 50.6 | |
| MDE-IDS (LGB)backbone=LightGBM combined2026.06 | — | — | — | — | — | — | — | — | — | 98.75 | — | — | — | |
| XGBoost2026.06 | — | — | — | — | — | — | — | — | — | 98.84 | — | — | — |