## 6 Results and Discussion
### Performance Evaluation Using the Feature Selection/Reduction Methods
The feature selection/reduction methods, the discretize filter, and the combination of these methods significantly reduced the model building time from 1137.71 s at the initial reading to its lowest reading at 269.69 s. Furthermore, the discretize filter classifier, the wrapper method in combination with the discretize filter, and the filter method in combination with the discretize filter significantly improved the performance of the SMO classifier from 97.40% detection accuracy and 1137.71 s model building time to 99.84% and 321.52 s, 99.47% and 372.68 s, and 99.68% and 269.69 s, respectively. These results suggest using the SMO classifier as an effective option for high-level detection accuracy but with an unreasonable model building time. Figures 5 and 6 present the performance of the SMO classifiers in terms of accuracy and model building time when used with different feature selection/reduction techniques. Table 9 demonstrates the performance of the RBF classifier based on various feature selection/reduction methods. It is clear that the discretize filter, the wrapper method in combination with the discretize filter, and the filter method in combination with the discretize filter increased  
Figure 5: SMO detection accuracy performance. Figure 6: SMO model building time performance. the model building time from 81.91 s at the initial reading to 163.07 s at the maximum. However, they enhanced the detection accuracy from 97.95% at the initial reading to 99.76% at the maximum. The discretize filter classifier improved the detection accuracy to 99.76% but compromised the model building time (163.07 s). Additionally, the wrapper method in combination with the discretize filter also enhanced detection accuracy to 99.48% but compromised the model building time (106.39 s). The filter method in combination with the discretize filter significantly improved the accuracy to 99.69% but compromised the model building time (127.68 s). Moreover, the wrapper method and the filter method significantly decreased the model building time but compromised the detection accuracy. Although the RBF classifier showed an increase in the model building time and maintained its high detection accuracy, it might still not be suitable for detecting intrusion in real-life situations. Figures 7 and 8 present the performance of the RBF classifier in terms of accuracy and model building time when used with different feature selection/reduction techniques. Table 10 shows that the performance of the BayesNet classifier was positively and negatively influenced by feature selection/reduction. The discretize filter significantly reduced the model building  
Figure 8: RBF model building time performance. Figure 7: RBF detection accuracy performance. time of the BayesNet classifier from 4.69 s at the initial reading to 0.41 s without compromising detection accuracy (97.18%). Furthermore, both the wrapper method in combination with the discretize filter and the filter method in combination with the discretize filter enhanced the model building time (1.68 s and 0.29 s, respectively) but compromised the detection accuracy (96.26% and 95.54%, respectively). Both the wrapper and filter methods also negatively affected the performance of the BayesNet classifier, decreased the detection accuracy, and increased the model building time. Thus, the authors suggest that using the BayesNet classifier with the discretize filter is appropriate to achieve low model building time with practical detection accuracy. Figures 9 and 10 show the performance of the BayesNet classifier in terms of accuracy and model building time when used with different feature selection/reduction methods. Table 11 presents the evaluation of the NBTree classifier performance based on different feature selection/reduction methods with or without the discretize filter.