## 6 Results and Discussion
### Classifier Comparison  
Phase 1 of this study shows that the NBTree classifier had the highest detection accuracy of 99.87%, and the REPTree classifier had the shortest model building time of 3.59 s. On the other hand, the LogitBoost classifier had the lowest detection accuracy of 97.10%, and the SMO classifier had the longest model building time of 1137.71 s when evaluated on the full NSL-KDD training dataset using 10-fold cross-validation in the Weka tool, as described in Table 2. Thus, it is clear that both the REPTree and NBTree classifiers had the best performance among the RBF, BayesNet, SMO, and LogitBoost classifiers even without the need for feature selection/reduction methods or discretization. Although NBTree outperformed the other tested classifiers in all measured parameters except in the model building time (213.18 s), its real-life efficiency might be affected by its very long model building time. By contrast, the REPTree classifier had the shortest model building time (3.59 s) among the tested classifiers; it had a very slightly compromised detection accuracy rate, but it had significantly high performance in terms of the TP rate, FP rate, ROC area, specificity %, and sensitivity %. Therefore, the authors suggest the use of the REPTree classifier as an appropriate option for intrusion detection, as it has a very high detection accuracy and short model building time.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ParametersClassifier** & **SMO** & **REPTree** & **NBTree** & **RBF** & **LogitBoost** & **BayesNet** \\ \hline Correctly Classified Instances & 122,704 & 125,766 & 125,819 & 123,394 & 122,329 & 122,409 \\  & 97.40\% & 99.83\% & 99.87\% & 97.95\% & 97.10\% & 97.17\% \\ Incorrectly Classified Instances & 3269 & 207 & 154 & 2579 & 3644 & 3564 \\  & 2.595\% & 0.164\% & 0.122\% & 2.0473\% & 2.892\% & 2.82\% \\ TP Rate & 0.974 & 0.998 & 0.999 & 0.980 & 0.971 & 0.972 \\ FP Rate & 0.028 & 0.002 & 0.001 & 0.022 & 0.030 & 0.032 \\ Precision & 0.974 & 0.998 & 0.999 & 0.980 & 0.971 & 0.972 \\ F-measure & 0.974 & 0.998 & 0.999 & 0.980 & 0.971 & 0.972 \\ ROC Area & 0.973 & 0.999 & 1.000 & 0.987 & 0.996 & 0.997 \\ Specificity (\%) & 96.0 & 99.8 & 99.81 & 96.8 & 96.2 & 94.6 \\ Sensitivity (\%) & 98.5 & 99.8 & 99.9 & 98.9 & 97.8 & 99.3 \\ Model Building Time (second) & 1137.71 s & 3.59 s & 213.18 s & 81.01 s & 18.3 s & 4.69 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of the six classifiers using 10-fold cross-validation.  
For further investigation, the tested classifiers were ranked from 1 to 6 based on their individual performance (Table 3). Rank 1 was considered the best, whereas rank 6 was considered the worst. Subsequently, the classifiers with lower numerical ranks were preferred over those with higher numerical ranks. Table 3 shows that the REPTree and NBTree classifiers outperformed the other tested classifiers, whereas the LogitBoost classifier performed the worst. Moreover, REPTree was the only classifier with the shortest model building time and very slightly compromised detection accuracy, TP rate, FP rate, precision, F-measure, ROC area, specificity, and sensitivity. Previous studies ranked the random forest classifier the best, as it outperformed nine other classifiers, but it consumed a huge amount of time (191.06 s) to build a model [4]. This indicates that REPTree outperforms random forest in terms of model building time.