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

Pseudocode 4

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

(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 filtering by removing the outliers and the extreme values
(4)	Conduct noise injection by manipulating the labels of the training instances (5, 10, 20, and 30%)
(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