UNSW-NB
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Intrusion Detection | UNSW-NB15 (test) | F1 Score95.29 | 33 | |
| Multi-classification | UNSW-NB15 | M.Acc (%)89.4 | 30 | |
| Binary classification | UNSW-NB15 | Binary Accuracy90.6 | 30 | |
| Intrusion Detection | UNSW-NB15 | Precision98.81 | 22 | |
| Knowledge Graph Completion | UNSW-NB15 (Transductive) | MR3.79 | 12 | |
| Classification | UNSW-NB15 | ASR91.08 | 9 | |
| Network Intrusion Detection | UNSW-NB15 | F1 Score97.1 | 5 | |
| Malicious Traffic Detection | UNSW-NB15 (without drift) | Accuracy99.67 | 5 | |
| Network Anomaly Detection | UNSW-NB15 5.76% Contamination | Accuracy97.63 | 4 | |
| Network Anomaly Detection | UNSW-NB15 (3.36% Contamination) | Accuracy97.67 | 4 | |
| Multi-class Classification | UNSW-NB15 (test) | F1 Score95.12 | 4 | |
| Language Modeling | UNSW-NB15 | Accuracy76.92 | 4 | |
| PU learning | UNSW-NB15 (test) | F1 Score85.65 | 4 | |
| Positive-Unlabeled Classification | UNSW-NB15 (test) | Accuracy82.24 | 4 | |
| DDoS Detection | UNSW-NB15 | Accuracy99.6 | 4 | |
| Intrusion Detection | UNSW-NB 2015 (average) | F1-macro23.45 | 3 | |
| Generative Modeling | UNSW-NB15 (test) | MMD0.3234 | 3 | |
| Network Intrusion Detection | UNSW-NB15 (unmodified) | Accuracy77.71 | 3 | |
| Anomaly Detection | UNSW-NB 15 | AUC80 | 3 |
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