A Framework for Identification and Classification of IoT Devices for Security Analysis in Heterogeneous Network

Table 3

Nonoptimal hyperparameter list.

First deep neural network
Input layer with activation function	22 neurons
Total hidden layers	3 hidden layers
First unseen (hidden) layer using the best activation function, Rectified Linear Unit (ReLu)	200 neurons
Second unseen (hidden) layer using the best activation function, Rectified Linear Unit (ReLu)	300 neurons
Third first unseen (hidden) layer using the best activation function, Rectified Linear Unit (ReLu)	100 neurons
Output layer with SoftMax activation function	2 neurons
Learning rate (LR)	0.01
Decay, momentum	, 0.9
Loss, optimizer	mean_squared_error, sgd
Epochs	99
Batch_size	35
Second deep neural network
Input layer with activation function	22 neurons
Total hidden layers	3 hidden layers
First unseen layer using a best activation function, Rectified Linear Unit (ReLu)	100 neurons
Second unseen (hidden) layer using a best activation function, Rectified Linear Unit (ReLu)	200 neurons
Third first unseen (hidden) layer using a best activation function, Rectified Linear Unit (ReLu)	100 neurons
Output layer with SoftMax activation function	6 neurons
Learning rate (LR)	0.01
Decay, momentum	, 0.9
Loss, optimizer	categorical_crossentropy, sgd
Epochs	99
Batch_size	35