Multi-class Network Intrusion Detection on CICIDS 2017
98.6AccuracyTMG-GAN
Evaluation results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| TMG-GANIDS model=CNN-BiLSTM2026.03 | 98.6 | — | — | — | — | |
| MultiCritics-WGAN-GPIDS model=CNN-BiLSTM2026.03 | 98.4 | — | — | — | — | |
| SYN-GANIDS model=CNN-LSTM2026.03 | 98.4 | — | — | — | — | |
| GMA-SAWGAN-GPIDS model=CNN-LSTM2026.03 | 98.4 | — | — | — | — | |
| TMG-GANIDS model=CNN-LSTM2026.03 | 98.3 | — | — | — | — | |
| WCGAN-GPIDS model=CNN-LSTM2026.03 | 98.3 | — | — | — | — | |
| SYN-GANIDS model=CNN-BiLSTM2026.03 | 98.2 | — | — | — | — | |
| DAE-GANIDS model=CNN-BiLSTM2026.03 | 98.2 | — | — | — | — | |
| DAE-GANIDS model=CNN-LSTM2026.03 | 98.2 | — | — | — | — | |
| MultiCritics-WGAN-GPIDS model=CNN-LSTM2026.03 | 98.2 | — | — | — | — | |
| GMA-SAWGAN-GPIDS model=CNN-BiLSTM2026.03 | 98.1 | — | — | — | — | |
| WCGAN-GPIDS model=CNN-BiLSTM2026.03 | 98 | — | — | — | — | |
| SYN-GANIDS model=CNN2026.03 | 97.8 | — | — | — | — | |
| DAE-GANIDS model=CNN2026.03 | 97.6 | — | — | — | — | |
| GMA-SAWGAN-GPIDS model=CNN2026.03 | 97.4 | — | — | — | — | |
| MultiCritics-WGAN-GPIDS model=CNN2026.03 | 97.3 | — | — | — | — | |
| WCGAN-GPIDS model=CNN2026.03 | 97.3 | — | — | — | — | |
| SYN-GANIDS model=LSTM2026.03 | 97.3 | — | — | — | — | |
| WCGAN-GPIDS model=LSTM2026.03 | 97.3 | — | — | — | — | |
| GMA-SAWGAN-GPIDS model=LSTM2026.03 | 97.3 | — | — | — | — | |
| MultiCritics-WGAN-GPIDS model=LSTM2026.03 | 96.9 | — | — | — | — | |
| TMG-GANIDS model=LSTM2026.03 | 96.9 | — | — | — | — | |
| TMG-GANIDS model=CNN2026.03 | 96.8 | — | — | — | — | |
| SYN-GANIDS model=DNN2026.03 | 96.8 | — | — | — | — | |
| DAE-GANIDS model=LSTM2026.03 | 96.8 | — | — | — | — | |
| GMA-SAWGAN-GPIDS model=DNN2026.03 | 96.7 | — | — | — | — | |
| DAE-GANIDS model=DNN2026.03 | 96.6 | — | — | — | — | |
| MetaTrafficFramework Type=NTC, Backbone=NetMamba+ [51]2026.05 | 96.4 | 96.37 | — | — | — | |
| MultiCritics-WGAN-GPIDS model=DNN2026.03 | 96 | — | — | — | — | |
| SWADFramework Type=General, Backbone=NetMamba+ [51]2026.05 | 95.98 | 95.93 | — | — | — | |
| TMG-GANIDS model=DNN2026.03 | 95.4 | — | — | — | — | |
| NetAugmentFramework Type=NTC, Backbone=NetMamba+ [51]2026.05 | 95.37 | 95.34 | — | — | — | |
| CDANNFramework Type=General, Backbone=NetMamba+ [51]2026.05 | 95.28 | 95.24 | — | — | — | |
| WCGAN-GPIDS model=DNN2026.03 | 95 | — | — | — | — | |
| SagNetFramework Type=General, Backbone=NetMamba+ [51]2026.05 | 94.94 | 94.89 | — | — | — | |
| UniAlignFramework Type=Ours, Backbone=NetMamba+ [51]2026.05 | 94.51 | 94.37 | — | — | — | |
| MMDFramework Type=General, Backbone=NetMamba+ [51]2026.05 | 93.53 | 93.37 | — | — | — | |
| StandardFramework Type=Original, Backbone=NetMamba+ [51]2026.05 | 92.81 | 92.47 | — | — | — | |
| RosettaFramework Type=NTC, Backbone=NetMamba+ [51]2026.05 | 90.92 | 90.6 | — | — | — | |
| DBN#Classes=6, Study=Our Study2022.07 | — | 94 | 99.7 | 88.7 | — | |
| DeepGFL#Classes=12, Study=[30]2022.07 | — | 53.1 | 44.8 | 94.8 | — | |
| KNN classifierSamples=102026.05 | — | 88.25 | 80.7 | 99.28 | 0.0264 | |
| KNN classifierSamples=202026.05 | — | 88.73 | 81.48 | 99.05 | 0.0246 | |
| KNN classifierSamples=402026.05 | — | 89.47 | 82.68 | 98.75 | 0.0375 | |
| KNN classifierSamples=802026.05 | — | 89.83 | 83.34 | 98.57 | 0.0412 | |
| KNN classifierSamples=1602026.05 | — | 90.22 | 84.31 | 98.03 | 0.0579 | |
| MLP#Classes=6, Study=Our Study2022.07 | — | 87.3 | 99.5 | 81.7 | — | |
| MLPSamples=102026.05 | — | 78.92 | 82.11 | 78.7 | 2.3389 | |
| MLPSamples=202026.05 | — | 78.92 | 82.11 | 78.7 | 2.3389 | |
| MLPSamples=402026.05 | — | 78.92 | 82.11 | 78.7 | 2.3389 | |
| MLPSamples=802026.05 | — | 78.92 | 82.11 | 78.7 | 2.3389 | |
| MLPSamples=1602026.05 | — | 78.92 | 82.11 | 78.7 | 2.3389 | |
| Siamese networkSamples=102026.05 | — | 63.12 | 80.6 | 62.83 | 20.8661 | |
| Siamese networkSamples=202026.05 | — | 67.35 | 81.27 | 65.12 | 15.48 | |
| Siamese networkSamples=402026.05 | — | 64.31 | 80.11 | 65.97 | 25.422 | |
| Siamese networkSamples=802026.05 | — | 70.23 | 83.23 | 66.95 | 11.8468 | |
| Siamese networkSamples=1602026.05 | — | 75.19 | 84.95 | 72.46 | 4.8695 | |
| triplet networkSamples=102026.05 | — | 87.57 | 79.67 | 99.16 | 0.0272 | |
| triplet networkSamples=202026.05 | — | 85.14 | 78.17 | 95.11 | 0.0204 | |
| triplet networkSamples=402026.05 | — | 89.57 | 82.6 | 99.17 | 0.0151 | |
| triplet networkSamples=802026.05 | — | 90.08 | 83.65 | 98.75 | 0.0312 | |
| triplet networkSamples=1602026.05 | — | 90.35 | 84.69 | 97.76 | 0.0615 |