Encrypted Traffic Classification on CIC-IoT 2022
99.9AccuracyMMAE
Evaluation results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| MMAEparadigm=Masked Autoencoder2026.03 | 99.9 | 99.9 | 99.9 | — | 99.9 | |
| NetMambaparadigm=Mamba-based2026.03 | 99.85 | 99.85 | 99.85 | — | 99.85 | |
| YaTCparadigm=Transformer-based2026.03 | 99.59 | 99.59 | 99.59 | — | 99.59 | |
| ET-BERTparadigm=Transformer-based2026.03 | 99.37 | 99.38 | 99.37 | — | 99.37 | |
| TrafficMoE2026.03 | 92.7 | 92.65 | 92.7 | 92.65 | — | |
| FlowletFormer2026.03 | 91.09 | 89.05 | 88.66 | 88.59 | — | |
| FlowletFormerparadigm=Transformer-based2026.03 | 91.09 | 89.05 | 88.66 | — | 88.59 | |
| TrafficFormer2026.03 | 87.25 | 84.87 | 83.43 | 82.88 | — | |
| TrafficFormerparadigm=Transformer-based2026.03 | 87.25 | 84.87 | 83.43 | — | 82.88 | |
| ET-BERT2026.03 | 86.03 | 82.97 | 82.55 | 82.44 | — | |
| AppScanner2026.03 | 85.91 | 88.58 | 79.96 | 82.88 | — | |
| YaTC2026.03 | 84.48 | 86.56 | 80.74 | 80.48 | — | |
| FSNet2026.03 | 80.77 | 82.5 | 83.33 | 78.04 | — | |
| AppScannerparadigm=Traditional Machine Learning2026.03 | 75.56 | 80.93 | 72.44 | — | 69.38 | |
| GraphDApp2026.03 | 73.7 | 67.21 | 70.06 | 67.67 | — | |
| BIND2026.03 | 73.49 | 67.54 | 63.87 | 64.35 | — | |
| CUMUL2026.03 | 70.19 | 67.46 | 70.29 | 66.87 | — | |
| FlowPrintparadigm=Traditional Machine Learning2026.03 | 58.2 | 41.64 | 58.2 | — | 46.43 | |
| FS-Netparadigm=CNN-based2026.03 | 57.47 | 38 | 57.47 | — | 42.16 | |
| DF2026.03 | 27.46 | 21.4 | 18.7 | 16.47 | — | |
| Beauty2026.03 | 13.56 | 3.49 | 7.64 | 2.96 | — |