Log Anomaly Detection on Casper (test)
100PrecisionCoLog
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| CoLogLearning paradigm=Supervised2025.12 | 100 | 100 | 100 | 100 | |
| Attentional BiLSTMLearning paradigm=Supervised2025.12 | 99.766 | 99.872 | 99.819 | 99.834 | |
| Convolutional Neural NetworkLearning paradigm=Supervised2025.12 | 99.766 | 99.872 | 99.819 | 99.834 | |
| pylogsentimentLearning paradigm=Supervised2025.12 | 99.487 | 99.413 | 99.449 | 99.459 | |
| TransformerLearning paradigm=Unsupervised2025.12 | 98.409 | 99.1 | 98.738 | 98.837 | |
| LSTMLearning paradigm=Unsupervised2025.12 | 97.973 | 98.843 | 98.38 | 98.505 | |
| Decision TreeLearning paradigm=Supervised2025.12 | 83.466 | 77.037 | 79.488 | 94.998 | |
| Logistic RegressionLearning paradigm=Supervised2025.12 | 66.959 | 60.863 | 59.7 | 90.993 | |
| Support Vector MachinesLearning paradigm=Supervised2025.12 | 58.614 | 60.757 | 59.482 | 90.967 | |
| Isolation ForestLearning paradigm=Unsupervised2025.12 | 52.407 | 50.65 | 49.926 | 88.149 | |
| Principal Component AnalysisLearning paradigm=Unsupervised2025.12 | 51.205 | 50.282 | 49.362 | 87.48 |