Hands-On Deep Learning for IoT

Use case two

We used an autoencoder for the multilayered IDS implementation using the KDD cup 1999 IDS dataset, and we have trained and tested the autoencoder on the three datasets. To train the model on each layer's dataset, we need to run the IDS_AutoEncoder_KDD.py file (available in the chapter's code folder) on the dataset as follows:

python IDS_AutoEncoder_KDD.py

We also trained and tested a DNN model on the overall KDD cup 1999 IDS dataset. To do so, we need to run the DNN-KDD-Overall.py file (available in the chapter's code folder) as follows:

python DNN-KDD-Overall.py

For all of the models, we have saved the best possible model to import and use in IoT devices. Also, we have saved models' logs using TensorBoard to visualize different ...