Log Anomaly Detection on Hadoop
99.997PrecisionCoLog
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| CoLogLearning Paradigm=Supervised2025.12 | 99.997 | 99.956 | 99.977 | 99.994 | |
| pylogsentimentLearning Paradigm=Supervised2025.12 | 99.886 | 99.732 | 99.809 | 99.905 | |
| LSTMLearning Paradigm=Unsupervised2025.12 | 99.85 | 97.397 | 98.589 | 99.715 | |
| Convolutional Neural NetworkLearning Paradigm=Supervised2025.12 | 99.719 | 99.847 | 99.783 | 99.955 | |
| Attentional BiLSTMLearning Paradigm=Supervised2025.12 | 97.64 | 97.955 | 97.792 | 97.902 | |
| TransformerLearning Paradigm=Unsupervised2025.12 | 97.28 | 99.833 | 98.518 | 99.685 | |
| Principal Component AnalysisLearning Paradigm=Unsupervised2025.12 | 49.995 | 49.996 | 49.996 | 58.214 | |
| Logistic RegressionLearning Paradigm=Supervised2025.12 | 48.523 | 50 | 49.25 | 97.046 | |
| Support Vector MachinesLearning Paradigm=Supervised2025.12 | 48.523 | 50 | 49.25 | 97.046 | |
| Decision TreeLearning Paradigm=Supervised2025.12 | 48.523 | 50 | 49.25 | 97.046 | |
| Isolation ForestLearning Paradigm=Unsupervised2025.12 | 47.702 | 50 | 48.824 | 54.034 |