## 6 Results and Discussion
### Performance Evaluation Using the Feature Selection/Reduction Methods  
In this section, the authors assessed the tested classifiers using different feature selection and reduction methods with or without discretization to determine whether their performance could be improved further. They used two combinations of feature selection/reduction methods, namely the wrapper method (CfsSubsetEval + BestFirst) and the filter method (InfoGainAttributeEval + Ranker), to improve the performance of six tested classifiers. Table 4 describes the details of the features selected by each combination in the second phase. The authors implemented the wrapper-based feature selection method on the full NSL-KDD training dataset to select the best subset of features. The method selected the optimal subset of 4, 5, 6, 12, 26, and 30 features from the full NSL-KDD training dataset (Table 4). Next, the authors ranked the datasets and omitted low-ranked features one by one until overfitting occurred. If a feature/attribute was further removed, the model started overfitting, and the percentage of correctly classified instances was reduced. Consequently, in the filter method, the authors removed features one after the other until global minima were reached. In the full NSL-KDD training dataset, the global minima were achieved when the authors were left with the top 10 features (5, 3, 6, 4, 30, 29, 33, 34, 35, and 38) for \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attribute Evaluator, Search Method, and Ranker** & **Features Selected** & **Method Used** \\ \hline CfsSubsetEval + BestFirst & 4, 5, 6, 12, 26, 30 & Wrapper method \\ \hline InfoGainAttributeEval + Ranker & 5, 3, 6, 4, 30, 29, 33, 34, 35, 38, 12, 3, 9, 25, 23, & \\ InfoGainAttributeEval + Ranker & 26, 37, 32, 36, 31, 24, 41, 2, 27, 40, 28, 1, 10, 8, & Filter method \\  & 13, 16, 19, 22, 17, 15, 14, 18, 11, 7, 21, 20, 9 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Feature selection/reduction using the wrapper method (CfsSubsetEval + BestFirst) and the filter method (InfoGainAttributeEval + Ranker) on the full NSL-KDD training dataset in the second phase of this study. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters Classifier** & **SMO** & **REPTree** & **NBTree** & **RBF** & **LogitBoost** & **BayesNet** \\ \hline Correctly Classified Instances & 4 & 2 & 1 & 3 & 6 & 5 \\ Incorrectly Classified Instances & 4 & 2 & 1 & 3 & 6 & 5 \\ TP Rate & 4 & 2 & 1 & 3 & 6 & 5 \\ FP Rate & 4 & 2 & 1 & 3 & 6 & 5 \\ Precision & 4 & 2 & 1 & 3 & 6 & 5 \\ F-measure & 4 & 2 & 1 & 3 & 6 & 5 \\ ROC Area & 6 & 2 & 1 & 5 & 4 & 3 \\ Specificity (\%) & 5 & 2 & 1 & 3 & 4 & 6 \\ Sensitivity (\%) & 5 & 2 & 1 & 4 & 6 & 3 \\ Model Building Time & 6 & 1 & 5 & 4 & 3 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ranks of the tested classifiers based on their reported performance in Table 2.  
detecting intrusion attacks (Table 5). The authors evaluated the performance of the ranked classifiers after eliminating all redundant features, as suggested by the wrapper and filter methods, and presented the results in Tables 5-11. The authors also used the discretize filter classifier individually and in combination with the wrapper and filter methods to evaluate the performance of the tested classifiers.