A Neighbor Prototype Selection Method Based on CCHPSO for Intrusion Detection

Algorithm 2

The proposed method.

	Input: training, validation, and testing datasets with labels, KNN as the main classifier, CCHPSO algorithm
	Output: testing accuracy (acc), DR, FPR, and confusion matrix.
(1)	Training:
(2)	Obtain the training, validation, and testing datasets by the stratification strategy
(3)	repeat
(4)	for each swarm
(5)	for each particle
(6)	fitness = KNN (pop, train scale, train label, validation scale, and validation label);
(7)	update the local and global Sol;
(8)	end for
(9)	Perform position and velocity updates using (2), (3), or (4)
(10)	end for
(11)	until termination is met;
(12)	Obtain the appropriate prototypes and feature weights according to the global optimal Sol.
(13)	Testing:
(14)	[testing accuracy, confusion matrix] = KNN (Sol, prototype data, prototype label, test scale, test label);