## 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** & **General** & **Discretize Filter** & **Wrapper** & **Wrapper Method** & **Filter** & **Filter Method +** \\
**Method** & **Classifier** & **Method** & **+ Discretize Filter** & **Method** & **Discretize Filter** \\ \hline Correctly Classified Instances & 122,409 & 122,422 & 121,270 & 121,274 & 119,540 & 120,367 \\  & 97.17\% & 97.18\% & 96.26\% & 96.26\% & 94.89\% & 95.54\% \\ Incorrectly Classified Instances & 3564 & 3551 & 4703 & 4699 & 6433 & 5606 \\  & 2.82\% & 2.818\% & 3.733\% & 3.730\% & 5.106\% & 4.450\% \\ TP Rate & 0.972 & 0.972 & 0.963 & 0.963 & 0.949 & 0.955 \\ FP Rate & 0.002 & 0.031 & 0.036 & 0.036 & 0.057 & 0.049 \\ Precision & 0.972 & 0.973 & 0.963 & 0.963 & 0.951 & 0.957 \\ F-measure & 0.972 & 0.972 & 0.963 & 0.963 & 0.949 & 0.955 \\ ROC Area & 0.997 & 0.998 & 0.995 & 0.997 & 0.997 & 0.997 \\ Specificity (\%) & 94.6 & 94.6 & 97.6 & 97.6 & 90.4 & 92.0 \\ Sensitivity (\%) & 99.3 & 99.3 & 95.0 & 95.0 & 98.7 & 98.6 \\ Model Building Time(second) & 4.69 s & 0.41 s & 5.64 s & 1.68 s & 4.95 s & 0.29 s \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance of the BayesNet classifier on the NSL-KDD training dataset using different feature selection/reduction methods. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Parameters** & **Method** & **Classifier** & **Wrapper** & **Wrapper Method** & **Filter** & **Filter Method +** \\
**Method** & **Classifier** & **Method** & **+ Discretize Filter** & **Method** & **Discretize Filter** \\ \hline Correctly Classified Instances & 122,2704 & 125,777 & 112,240 & 125,311 & 121,651 & 125,578 \\  & 97.40\% & 99.84\% & 89.09\% & 99.47\% & 96.56\% & 99.68\% \\ Incorrectly Classified Instances & 3269 & 196 & 13,733 & 662 & 4322 & 395 \\  & 2.595\% & 0.155\% & 10.901\% & 0.525\% & 3.430\% & 0.313\% \\ TP Rate & 0.974 & 0.998 & 0.891 & 0.995 & 0.966 & 0.997 \\ FP Rate & 0.028 & 0.002 & 0.117 & 0.006 & 0.035 & 0.003  
in combination with the discretize filter (0.6 s). These improvements occurred without significantly compromising the detection accuracy range (99.44-99.78%), suggesting the use of the REPTree classifier with either the discretize filter classifier or the filter method in combination with the discretize filter as an efficient tool in an IDS. Researchers can consider this an additional and feasible classifier option with high detection accuracy and low model building time. Figures 1 and 2 are the diagrams of the detection accuracy and model building time of the REPTree classifier with different feature selection/reduction methods. Table 7 shows the evaluation of the LogitBoost classifier using different feature selection/reduction methods with or without the discretize filter. As shown in the table, feature selection/reduction methods, the discretize filter, and the combination of the methods significantly reduced the model building time from 18.3 s at the initial point to the lowest limit of 0.98 s. However, this compromised detection accuracy, except for the filter method in combination with the discretize filter, which showed slightly higher detection accuracy (97.20%) than the initial one (97.10%) with lower model building time (0.98 s). Therefore, the authors recommend the use of the LogitBoost classifier with the filter method in combination with the discretize filter for better performance. Figures 3 and 4 illustrate the performance of the LogitBoost classifier in terms of accuracy and model building time when used with different feature selection/reduction methods. Figure 4: LogitBoost model building time performance. Figure 3: LogitBoost detection accuracy performance. Figure 2: REPTree model building time performance. Table 8 presents the evaluation of the SMO classifier's performance based on different feature selection/reduction methods with or without the discretize filter.