A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets

Algorithm 1

Strategy for implementing the stacking ensemble.

	Input: Train data
	Output: Predictions from the ensemble E
	Step 1. Impose cross validation in order to prepare a training set for meta-classifier
	Step 2. Randomly split T into “m” equal size subsets, i.e.,
	Step 3. for to M
	Learn base classifiers namely random forest, KNN, and logistic regression
	for to N
	Learn a classifier Pmn from T or Tm
	End for
	Step 4. Formulate a training set for metaclassifier (SVM)
	for each Xi ϵ Tm
	Extract a new instance (xi’, yi), where xi’ =
	End for
	End for
	Step 5. Return from ensemble