## 6 Results and Discussion
### Performance Evaluation Using the Feature Selection/Reduction Methods
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Parameters** & **Methods** & **General** & **Discretize Filter** & **Wrapper Method** & **Filter** & **Method +** \\  & **Method** & **Classifier** & **Method** & **+** & **Discretize Filter** & **Method** & **Discretize Filter** \\ \hline Correctly Classified Instances & 125,766 & 125,665 & 125,724 & 125,382 & 125,706 & 125,637 \\  & 99.83\% & 99.75\% & 99.44\% & 99.53\% & 99.78\% & 99.73\% \\  & 207 & 308 & 699 & 591 & 267 & 336 \\ Incorrectly Classified Instances & 0.164\% & 0.244\% & 0.554\% & 0.469\% & 0.212\% & 0.266\% \\ TP Rate & 0.998 & 0.998 & 0.994 & 0.995 & 0.998 & 0.997 \\ FP Rate & 0.002 & 0.002 & 0.006 & 0.005 & 0.002 & 0.003 \\ Precision & 0.998 & 0.998 & 0.994 & 0.995 & 0.998 & 0.997 \\ F-measure & 0.998 & 0.998 & 0.994 & 0.995 & 0.998 & 0.997 \\ ROC Area & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 & 0.999 \\ Specificity (\%) & 99.8 & 99.7 & 99.7 & 99.2 & 99.7 & 99.7 \\ Sensitivity (\%) & 99.8 & 99.7 & 99.7 & 99.7 & 99.8 & 99.7 \\ Model Building Time (second) & 3.59 s & 1.93 s & 5.76 s & 1.62 s & 4.65 s & 0.6 s \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of the REPTree classifier on the NSL-KDD training dataset using different feature selection/reduction methods. \begin{table}
\begin{tabular}{l l} \hline \hline
**Number of Global Minima** & **Feature Name and Description** \\ \hline \multirow{2}{*}{5} & Src\_bytes: Bytes transferred in a single connection from the source to the destination \\ \hline \multirow{2}{*}{6} & Dst\_bytes: Bytes transferred in a single connection from the source to the destination \\ \cline{2-2}  & Dst\_bytes: Bytes transferred in a single connection from the source to the destination \\ \hline \multirow{2}{*}{30} & Dift\_sv\_rate: Rate of connections among the connections collected in count (23) that were to different services \\ \cline{2-2}  & Same\_svr\_rate: Rate of connections among the connections collected in count (23) that were to the same service \\ \hline \multirow{2}{*}{33} & Dst\_host\_svr\_count: Number of connections with a similar port number \\ \cline{2-2}  & Dst\_host\_same\_svr\_rate: Connection rate among the connections \\ \cline{2-2}  & collected in dst\_host\_count (32) that were to the same service \\ \hline \multirow{2}{*}{35} & Dst\_host\_diff\_svr\_rate: Connection rate among the connections collected in dst\_host\_count (32) that were to different services \\ \cline{2-2}  & Dst\_host\_svr\_rate: Connection rate among the connections collected in dst\_host\_count (32) that have activated the flag (4) s0, s1, s2, or s3 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: List of the highest-ranked features by the filter method for the tested ML classifiers. The filter methods showed that the tested ML classifiers were left with the same top 10 features in the same order (Table 5). This implies that all classifiers were run on the same dataset and conditions simultaneously, allowing a fair comparison of their efficacies and performances. Table 5 shows the global minimum, name, and description of each of the top 10 features ranked with the filter method. Table 6 shows the evaluation of REPTree classifier performance based on different methods. The authors observed that the detection accuracy of REPTree was initially 99.83% with a 3.59 s model building time. However, after the different combinations of feature selection/reduction techniques were applied, the model building time was reduced significantly by the discretize filter classifier (1.93 s), the wrapper method in combination with the discretize filter (1.62 s), and the filter method