[Retracted] Research on Boruta-ET-Based Anomalous Traffic Detection Model

Algorithm 1

Extreme trees.

	Input: Train set
	Output: Extreme Trees
(1)	for i = 1 to M do
(2)	Generating decision trees,
(3)	Return Extreme Trees T
(4)	end for
	Build_an_extra_tree(D)
	Input: Train data
	Output: Decision Tree t
(1)	if or all candidate attributes in D are constant or output variables in D are constant then
(2)	Return a leaf node
(3)	else
(4)	Randomly select K attributes from all candidate attributes
(5)	Generate K split thresholds , Among them
(6)	According to , Selecting the best test split threshold
(7)	According to test split thresholds , Divide the sample set D into two sub-sample sets and
(8)	Construct a left subtree and a right subtree using subsets and respectively
(9)	Create a tree node based on , with and as its left and right subtrees respectively, and return a decision tree t
(10)	end if
	Input: Train data , Attributes a
	Output: Divided attributes
(1)	Calculate the minimum and maximum values of attribute a in the training set D, denoted respectively as and
(2)	Select a random splitting attribute from
(3)	Return to Split attributes