Improving the Accuracy of Network Intrusion Detection with Causal Machine Learning

Algorithm 1

Causal reasoning-based feature selection (CRFS).

	Input: , and set P represents the features set, which contains N features
	Output: , and is a causal feature set, which contains features
(1)	// represents the maximum set of deleted features
(2)	// represents the set of features that have been deleted from the ith feature in Set P
(3)	for i from 1 to N
(4)	for j from i to N + i-1
(5)
(6)	if
(7)	Delete the feature
(8)	//Noise features numbers are stored in sets
(9)	end if
(10)	end for
(11)	end for
(12)	Count = []; it represents a set of noise features
(13)	for i from 1 to N//. Compare the set of features of all Cun[i] and assign the set with the most noise features set to Count
(14)	if
(15)	then
(16)	end if
(17)	end for
(18)	for i from 0 to len (Count)
(19)	Delete all noise features in the Cun[i] collection;
(20)	end for
(21)	output the causal feature set C.