Empirical Evaluation of Noise Influence on Supervised Machine Learning Algorithms Using Intrusion Detection Datasets

Pseudocode 5

The fifth set of experiments (noise injection then filtering).

(1)	Load the training portion of the intrusion dataset
(2)	Convert the label values from numeric to nominal (only for UNSW-NB15)
(3)	Conduct noise injection by manipulating the labels of the training instances (5, 10, 20, and 30%)
(4)	Conduct noise filtering by removing the outliers and the extreme values
(5)	Train the ML algorithm on the noisy training dataset
(6)	Run the ML algorithm on the testing dataset
(7)	Document the results
(8)	Revert to step 4 until each of the ML algorithms is trained and tested with the different levels of noise