Log Anomaly Detection on Honey7
1PrecisionAttentional BiLSTM
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| Attentional BiLSTMSupervision type=Supervised2025.12 | 1 | 1 | 1 | 1 | |
| Convolutional Neural NetworkSupervision type=Supervised2025.12 | 1 | 1 | 1 | 1 | |
| CoLogSupervision type=Supervised2025.12 | 1 | 1 | 1 | 1 | |
| pylogsentimentSupervision type=Supervised2025.12 | 0.9997 | 0.9911 | 0.9954 | 0.9994 | |
| TransformerSupervision type=Unsupervised2025.12 | 0.9692 | 0.9905 | 0.9793 | 0.9852 | |
| LSTMSupervision type=Unsupervised2025.12 | 0.9621 | 0.9881 | 0.9743 | 0.9816 | |
| Decision TreeSupervision type=Supervised2025.12 | 0.9326 | 0.8331 | 0.8636 | 0.9747 | |
| Logistic RegressionSupervision type=Supervised2025.12 | 0.6814 | 0.7 | 0.6904 | 0.9629 | |
| Support Vector MachinesSupervision type=Supervised2025.12 | 0.6814 | 0.7 | 0.6904 | 0.9629 | |
| Principal Component AnalysisSupervision type=Unsupervised2025.12 | 0.6087 | 0.589 | 0.5594 | 0.8828 | |
| Isolation ForestSupervision type=Unsupervised2025.12 | 0.4751 | 0.5095 | 0.4806 | 0.8597 |