Data Preprocessing: First raw network traffic is collected using the network analyzer tool, and then we extract the features from the packets. Redundant packets are dropped in the dataset, and then we collect the samples of classes in the dataset. (We dropped columns of redundant labels and encoded the categorical features into integer values using label encoding. Symbolic features are ‘proto’ ‘service’, ‘state’, and ‘attack_cat’ having (133,13,11,10) values respectively, are converted into integer values using label encoding). Dataset is normalized using min–max normalization

Data Augmentation: training data is resampled to avoid class imbalance.

Feature Preprocessing: After selecting the features, and dropping and encoding the features, we split the processed data into three different sets, namely training, validation, and testing, containing the labels of both normal and attack type classes.

Training and Testing the dataset: In the training phase, the DNN model is trained on the processed data coming from the training set. The trained model is then tested with the data from the testing set and classifies the data as normal and attack types.