Research Article
A Framework for Identification and Classification of IoT Devices for Security Analysis in Heterogeneous Network
Table 4
Optimal hyperparameters list values.
| | First deep neural network | | Input layer with activation function | 22 neurons | | Total hidden layers | 4 hidden layers | | First unseen (hidden) layer using the best activation function, Rectified Linear Unit (ReLu) | 300 neurons | | Second unseen (hidden) layer using the best activation function, Rectified Linear Unit (ReLu) | 500 neurons | | Third first unseen (hidden) layer using the best activation function, Rectified Linear Unit (ReLu) | 150 neurons | | Fourth unseen (hidden) layer using the best activation function, Rectified Linear Unit (ReLu) | 300 neurons | | Output layer with SoftMax activation function | 2 neurons | | Learning rate (LR) | 0.0001 | | Decay, momentum | , 0.9 | | Loss, optimizer | mean_squared_error, sgd | | Epochs | 3800 | | Batch_size | 15 |
| | Second deep neural network | | Input layer with activation function | 22 neurons | | Total hidden layers | 4 hidden layers | | First unseen layer using the best activation function, Rectified Linear Unit (ReLu) | 300 neurons | | Second unseen (hidden) layer using the best activation function, Rectified Linear Unit (ReLu) | 500 neurons | | Third first unseen (hidden) layer using the best activation function, Rectified Linear Unit (ReLu) | 700 neurons | | Fourth unseen (hidden) layer using the best activation function, Rectified Linear Unit (ReLu) | 300 neurons | | Output layer with SoftMax activation function | 6 neurons | | Learning rate (LR) | 0.00001 | | Decay, momentum | , 0.9 | | Loss, optimizer | categorical_crossentropy, sgd | | Epochs | 3800 | | Batch_size | 30 |
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