Research Article
Network Traffic Anomaly Detection Based on ML-ESN for Power Metering System
Table 4
The parameters of ML-ESN experiment.
| Parameters | Values |
| Input dimension number | 5 | Output dimension number | 10 | Reservoir number | 3 | Reservoir neurons number | 1000 | Reservoir activation fn. | Tanh | Output layer activation fn. | Sigmoid | Update rate | 0.9 | Random seed | 50 | Regularization rate | |
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