## 5 Experimental Setup
### Evaluation Time
The testing time for the default attributes ranges between 0.033 and 5.214 s, while the testing time for the reduced 13 features obtained through feature selection ranges between 0.011 and 0.157 s, except for the neural network, which has a testing time of 4.386 s. The plot also shows that the default attributes for the DNN technique have a testing time of 5.240 s, which is significantly longer than the testing time for the reduced 13 features obtained through feature selection, indicating the effectiveness of feature selection in reducing testing time. Moreover, the difference in feature selection between the default attributes and the reduced 13 features is quite significant for the DNN technique, with a difference of 48.190 s. This further emphasizes the importance of feature selection in optimizing the performance of the DNN technique. The plot in Figure 12, which compares the training time of a brute force attack by applying defaults to 34 attributes with respect to the reduced 17 features obtained from the feature analysis of the MQTT dataset. The default attributes have a range of training time between 5244.973 and 6238.328 s, while the training time for the reduced 17 features obtained through feature selection ranges between 139.586 and 236.027 s. It is noted that the DNN technique has good performance in default attributes, with a training time of 26.1600 s. However, in feature selection, DNN took a significantly longer time of 546.750 s compared to other algorithms, indicating that it is more expensive in terms of computation resources. Overall, the plot shows that the reduced 17 features obtained through feature selection significantly reduce the training time compared to using default attributes, except for the DNN technique, which takes longer time for feature selection. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Learning Algorithms** & **DoS Attack** & **Brute-Force Attack** & **Malformed Attack** \\
**Training Time (s)** & & & \\ \hline NB & 59.81(4) & 153.66(3) & 271.17(3) \\ DT & 60.3(5) & 154.02(4) & 271.57(4) \\ NN & 47.02(2) & 139.59(1) & 262.42(1) \\ RF & 58.7(3) & 152.89(2) & 270.38(2) \\ GB & 115.45(7) & 232.52(6) & 308.43(6) \\ LR & 122.44(8) & 236.03(7) & 311.84(7) \\ MLP & 107.31(6) & 230.44(5) & 305.93(5) \\ DNN & 7.67(1) & 546.75(8) & 402.84(8) \\ NB & 0.0248(2) & 0.0131(2) & 0.0125(2) \\ DT & 0.0258(3) & 0.0149(3) & 0.0157(3) \\ NN & 4.3855(7) & 8.7942(7) & 2.7996(7) \\ RF & 0.7298(6) & 0.4388(6) & 0.4777(6) \\ GB & 0.1035(4) & 0.0295(4) & 0.0331(4) \\ LR & 0.0115(1) & 0.0067(1) & 0.0106(1) \\ MLP & 0.1565(5) & 0.0601(5) & 0.1021(5) \\ DNN & 48.19(8) & 27.66(8) & 18.84(8) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Rank comparison matrix table of techniques to show the gain achieved using FS w.r.t. training and testing time. Figure 12: Compare Training CPU time in sec w.r.t. Brute force attack between defaults attributes and feature selection. The plot in Figure 13 shows a comparison of testing time in a brute force attack by applying defaults to 34 attributes with respect to the reduced 17 features that are obtained in the feature analysis of the MQTT dataset as described in Section 4.6. Default attributes range between 0.022 and 2.715 s in testing time and the feature selection is between 0.007 and 8.794 s. A comparison of CPU time in seconds for a BF (brute force) attack on a dataset. The dataset is analyzed using default attributes, which range between 0.022 and 2.715 s in testing time. The dataset is also analyzed using feature selection, which reduces the dataset to 16 features and results in testing times ranging between 0.007 and 8.794 s. Additionally, a DNN (deep neural network) was used to analyze the dataset using default attributes, which took 3.270 s in testing time. The same DNN was also used to analyze the dataset after feature selection, which took 27.660 s in testing time. The paragraph notes that the increase in testing time for the DNN after feature selection was significant.