Log Anomaly Detection on Nssal
99.955PrecisionCoLog
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| CoLogLearning paradigm=Supervised2025.12 | 99.955 | 99.915 | 99.935 | 99.967 | |
| pylogsentimentLearning paradigm=Supervised2025.12 | 97.17 | 96.05 | 96.602 | 99.02 | |
| Attentional BiLSTMLearning paradigm=Supervised2025.12 | 96.75 | 98.805 | 97.754 | 99.813 | |
| Convolutional Neural NetworkLearning paradigm=Supervised2025.12 | 96.703 | 98.243 | 97.46 | 99.789 | |
| TransformerLearning paradigm=Unsupervised2025.12 | 96.304 | 99.354 | 97.778 | 99.813 | |
| LSTMLearning paradigm=Unsupervised2025.12 | 96.148 | 97.669 | 96.896 | 99.742 | |
| Decision TreeLearning paradigm=Supervised2025.12 | 94.791 | 87.7 | 89.47 | 98.063 | |
| Logistic RegressionLearning paradigm=Supervised2025.12 | 85.133 | 74.728 | 76.476 | 97.604 | |
| Support Vector MachinesLearning paradigm=Supervised2025.12 | 80.206 | 74.935 | 76.474 | 97.655 | |
| Isolation ForestLearning paradigm=Unsupervised2025.12 | 65.504 | 57.352 | 56.101 | 80.967 | |
| Principal Component AnalysisLearning paradigm=Unsupervised2025.12 | 52.642 | 53.827 | 49.505 | 80.614 |