Research Article
FCNN: An Efficient Intrusion Detection Method Based on Raw Network Traffic
| Sequence | Description |
| 1 | Convolution layer (48 units, kernel size: 3, activation function: ReLU) | 2 | Dropout layer (dropout rate: 0.1) | 3 | Convolution layer (48 units, kernel size: 3, activation function: ReLU) | 4 | Pooled layer (size: 2) | 5 | Convolution layer (128 units, kernel size: 3, activation function: ReLU) | 6 | Dropout layer (dropout rate: 0.1) | 7 | Convolution layer (128 units, kernel size: 3, activation function: ReLU) | 8 | Pooled layer (size: 2) | 9 | Flatten layer | 10 | Dense layer (128 units, kernel size: 3, activation function: ReLU) | 11 | Dropout layer (dropout rate: 0.1) | 12 | Dense layer (1 unit, activation function: sigmoid) |
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