Algorithm 2: Bagging classifier algorithm.

Input: KDD99 and NSLKDD datasets Training: Selection of the number of samples for Bagging which is n samples and also the selection of base classifier C (j48, Random Forest, and reptree in our case). Dividing dataset into two subsets (Training and Testing subsets). Produce further training datasets using with replacement sampling and these datasets are $D_1{D}_2{D}_3.........D_n$. Then, train a base classifier on each dataset $D_i$ and build n number of classifiers $C_1{C}_2{C}_3.........C_n$. Testing: In the testing dataset, each data object X is passed to trained classifiers $C_1{C}_2{C}_3.........C_n$. The label is assigned to every new data object based on a majority vote. For the classification problem, the majority vote is used to assign a new label to data point X and, for the regression problem, the average value is used to be assigned to a new data object $X_i$. We repeat these steps until we classify every object in the dataset.