A Network Data Reinforcement Method Based on the Multiclass Variational Autoencoder

Algorithm 1

Data Preprocessing.

	def pre_processing (phase):
	if (phase = = ‘before MCVAE’):
	data = get_data() # get the dataset to be processed
	data = features_selected (data, features) # select features uses for training
	data = z_score (data) # scale data with z_score algorithm
	elif (phase = = ‘before Model Training’):
	data = label_encode (data) # map labels to 0 (benign) and 1 (nonbenign)
	else:
	print (‘error’) # if the function is used in other phases, an
	return 0 error will return
	return data # output the processed dataset