Network Intrusion Anomaly Detection Model Based on Multiclassifier Fusion Technology

Algorithm 3

Naive Bayes

	Input: the results set predicted by each base classifiers Y = {y1, y2, …, yn}, confusion matrix set E = {e^1, e^2, …, e^n}, the number of samples of each types M = {m0, m1};
	Output: final prediction result C;
(1)	a [2] ← {0, 0};
(2)	for i in {0, 1} do
(3)	c i ← 1;
(4)	for j in {1, 2, …, n} do
(5)	k ← yj;
(6)	c i ← cie^jik;
(7)	end for
(8)	a [i] ← mici;
(9)	end for
(10)	C ← argmax (a);
(11)	return C;