Figure 1:
Hybrid LSTM-GRU Intrusion Detection Framework
Figure 2:
Long Short Term Memory Network
Figure 3:
Gated Recurrent Unit Network
Table 1
Hyperparameter Tuning for proposed Model
| Learning Rate | No. of units | Dropout Rate | Optimizer |
|---|---|---|---|
| 0.001 | 16 | 0.2 | Adam |
| 0.01 | 32 | 0.3 | SGD |
| 0.1 | 64 | 0.5 | RMSProp |
Figure 4:
Hyperparameter tuning Grid Search Architecture
Table 2:
Evaluation of Performance Metrics
| Performance Measures | Expression |
|---|---|
| Accuracy | |
| Precision | |
| Recall | |
| F1-Score | |
| Specificity |
Table 3:
Comparitive Analysis between different models for intrusion detection
| Algorithm | Accuracy | Precision | Recall | F1-Score | Specificity |
|---|---|---|---|---|---|
| CNN | 0.75 | 0.78 | 0.78 | 0.79 | 0.78 |
| RNN | 0.77 | 0.8 | 0.79 | 0.8 | 0.8 |
| LSTM | 0.85 | 0.86 | 0.86 | 0.87 | 0.88 |
| GRU | 0.89 | 0.9 | 0.9 | 0.89 | 0.9 |
| Proposed Model | 0.95 | 0.96 | 0.95 | 0.96 | 0.95 |
Figure 5:
Comparative Analysis of Different Deep Learning Models Based on Evaluating the Accuracy
Figure 6:
ROC Curve for different models in intrusion detection