## Appendix A Detailed Results  
This Appendix presents the detail results, i.e., evaluation metrics values, graphs, and ROC curves, for all the three datasets used in this study. Tables A1 and A2 show the detailed results for the supervised and unsupervised (both individual and ensemble) models for the NSL-KDD dataset. Figure A3a,b show the corresponding bar graphs, and the ROC curves are shown in Figure A2. Tables A3 and A4 show the detailed results for the supervised and unsupervised (both individual and ensemble) models for the UNSW-NB15 dataset. Figure A3a,b show the corresponding bar graphs, and the ROC curves are shown in Figure A4. Tables A5 and A6 show the detailed results for the supervised and unsupervised (both individual and ensemble) models for the CICIDS2017 dataset. Figure A5a,b show the corresponding bar graphs, and the ROC curves are shown in Figure A6. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Methodology** & **Ensemble Mechanism** & **Dataset** & **Accuracy** & **Precision** & **Recall** & **FPR** \\ \hline Ensemble[6] & Adaptive Voting & NSL-KDD & 85.20\% & - & - & - \\ Ensemble[7] & Bagging & NSL-KDD & 84.25\% & - & - & - \\ Ensemble[8] & DAREnsemble & NSL-KDD & 78.80\% & - & - & - \\ Supervised [16] & RF & CIC-IDS2017 & 96.13\% & - & - & - \\ Ensemble [23] & Adaboosting & CIC-IDS2017 & 99\% & - & - & 0.50\% \\ Semisupervised [18] & Extra-Trees & UNSW-NB15 & 93.71\% & - & - & 0.46\% \\ Our Work & Supervised and Unsupervised & NSL-KDD & 97.50\% & 99.10\% & 95.20\% & 0.60\% \\ Our Work & Supervised and Unsupervised & UNSW-NB15 & 98.60\% & 98.20\% & 97.60\% & 0.09\% \\ Our Work & Supervised and Unsupervised & CICIDS-2017 & 99.90\% & 99.90\% & 99.90\% & 0.10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A performance comparison of our method with that of existing ensemble methods. Figure A2: ROC curves for all the models running on the NSL-KDD dataset. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Models** & **F1 Score** & **Accuracy** & **Precision** & **Recall** & **FPR** & **ROC AUC** &
\begin{tabular}{} **Elapsed** \\ **Time (s)** \\ \end{tabular} \\ \hline LR & 0.946 & 0.964 & 0.963 & 0.930 & 0.019 & 0.956 & 1.24 \\ NB & 0.800 & 0.837 & 0.684 & 0.964 & 0.227 & 0.868 & 0.19 \\ NN & 0.831 & 0.900 & 0.974 & 0.724 & 0.010 & 0.87 & 1.29 \\ DT & 0.978 & 0.985 & 0.977 & 0.979 & 0.012 & 0.984 & 0.28 \\ SVM & 0.969 & 0.979 & 0.958 & 0.981 & 0.022 & 0.980 & 23.04 \\ Ens\_MV & 0.972 & 0.981 & 0.964 & 0.981 & 0.019 & N/A & 28.25 \\ Ens\_LR & 0.979 & 0.986 & 0.982 & 0.976 & 0.009 & 0.983 & 26.29 \\ Ens\_NB & 0.971 & 0.980 & 0.961 & 0.982 & 0.021 & 0.981 & 26.25 \\ Ens\_NN & 0.978 & 0.985 & 0.964 & 0.933 & 0.019 & 0.987 & 26.66 \\ Ens\_DT & 0.978 & 0.985 & 0.961 & 0.996 & 0.021 & 0.987 & 26.26 \\ Ens\_SVM & 0.978 & 0.985 & 0.961 & 0.996 & 0.021 & 0.988 & 26.70 \\ \hline \hline \end{tabular}
\end{table} 
Table A3: Performance metrics for UNSW-NB15 dataset using supervised ensemble framework. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Models** & **F1 Score** & **Accuracy** & **Precision** & **Recall** & **FPR** & **ROC AUC** &
\begin{tabular}{} **Elapsed** \\ **Time (s)** \\ \end{tabular} \\ \hline OCSVM\_P & 0.561 & 0.608 & 0.451 & 0.742 & 0.461 & 0.641 & 35.58 \\ OCSVM\_L & 0.565 & 0.741 & 0.654 & 0.498 & 0.135 & 0.682 & 15.69 \\ LOF & 0.499 & 0.514 & 0.383 & 0.716 & 0.590 & 0.563 & 11.83 \\ ISOF & 0.596 & 0.541 & 0.424 & 1.000 & 0.693 & 0.653 & 246.25 \\ EE & 0.855 & 0.865 & 0.747 & 1.000 & 0.173 & 0.913 & 3.890 \\ Ens\_MV & 0.624 & 0.676 & 0.514 & 0.795 & 0.385 & N/A & 315.4 \\ Ens\_LR & 0.896 & 0.925 & 0.851 & 0.945 & 0.085 & 0.930 & 313.6 \\ Ens\_NB & 0.855 & 0.884 & 0.746 & 1.000 & 0.176 & 0.912 & 313.6 \\ Ens\_NN & 0.841 & 0.875 & 0.744 & 0.967 & 0.172 & 0.897 & 313.5 \\ Ens\_DT & 0.896 & 0.925 & 0.851 & 0.945 & 0.085 & 0.930 & 313.6 \\ Ens\_SVM & 0.896 & 0.925 & 0.851 & 0.945 & 0.085 & 0.930 & 315.6 \\ \hline \hline \end{tabular}
\end{table} 
Table A4: Performance metrics for UNSW-NB15 test dataset using unsupervised ensemble framework.