## 4 Experiment Results
### Detection Results  
Table 6 shows the detection results for all sets of selected features. Of the 24 selected features, CTDI achieves the best results with 99.92% accuracy and a 99.52% F1 score, which are very convincing for detecting AWID wireless attacks. For the originally selected feature sets, they also achieved very good results in accuracy and F1 score. This shows these selected features are very representative and match the observation of feature selection results. CTFD shows the best results for the original selected feature sets. For the Auto Encoder features, CTMD also shows the best detection results, so the Auto Encoder selected features are also very helpful.  
The results show that the selected features representing the high differences between normal and attack classes are very effective in distinguishing between normal and attack behaviors in wireless networks. CTDI accumulates the best selected features from the original and Auto Encoder features, and thus it achieves the best convincing detection results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy (\%)** & **F1 (\%)** & **Precision (\%)** & **Recall (\%)** \\ \hline CTDI & 99.92\% & 99.52\% & 99.98\% & 99.06\% \\ \hline \multicolumn{5}{c}{Original Feature} \\ \hline CTFD & 98.31\% & 90.08\% & 98.32\% & 83.12\% \\ \hline CTMD & 98.07\% & 88.72\% & 97.14\% & 81.64\% \\ \hline CTDD & 97.10\% & 83.29\% & 92.74\% & 75.58\% \\ \hline \multicolumn{5}{c}{Auto Encoder Feature} \\ \hline CTFD & 94.65\% & 73.11\% & 93.35\% & 60.09\% \\ \hline CTMD & 98.25\% & 89.52\% & 95.86\% & 83.97\% \\ \hline CTDD & 97.43\% & 85.03\% & 93.75\% & 77.80\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Detection results from all sets of selected features.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Original Feature**} \\ \hline
**No.** & **Name** & **No.** & **Name** & **No.** & **Name** \\ \hline
4 & frame.time\_epoch & 51 & radiotap.channel.type.ofdm & 71 & wlan.fc.pwrmgt \\
7 & frame.time\_relative & 64 & wlan.fc.type\_subtype & 73 & wlan.fc.protected \\
8 & frame.len & 66 & wlan.fc.type & 118 & wlan\_mgt.tagged.all \\
9 & frame.cap\_len & 67 & wlan.fc.subtype & 140 & wlan.wep.iv \\
47 & radiotap.datarate & 68 & wlan.fc.ds & 142 & wlan.wep.icv \\
50 & radiotap.channel.type.cck & 70 & wlan.fc.retry & 154 & data.len \\ \hline \multicolumn{5}{c}{**Auto Encoder Feature**} \\ \hline
8 & frame.len & 66 & wlan.fc.type & 78 & wlan.ta \\
39 & radiotap.flags.cfp & 76 & wlan.ra & 139 & wlan\_mgt.tcprep.link\_mrg \\ \hline \hline \end{tabular}
\end{table}
Table 5: The names of selected original and Auto Encoder features in CTDI.  
The EFS in CTDI adopts the majority voting approach to select the features from the three indexes as two of the indexes agree with the same feature. Other related works adopting the ensemble approach usually select several machine learning methods, such as Random Forest and Mutual Information [4], Decision Tree 4.5, Support Vector Machine, and Artificial Neural Network [17]. However, they do not merge the selected features from the different feature selection methods. The results show that CTDI achieves better results than CTFD, CTMD, and CTDD, and thus CTDI shows the merged feature sets by EFS are more effective than the related works, which only use one feature selection method to generate one separated selected feature set individually.