## 4 Experimental Results and Analysis
### Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **MI Score** & **Feature** & **MI Score** & **Feature** & **MI Score** \\ \hline Cat\_Normal & 0.694397 & Sub\_Cat\_Normal & 0.693928 & Src\_IP & 0.693317 \\ Dst\_IP & 0.573648 & Src\_Port & 0.559126 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Top Features with MI score \(\geq\) 0.5. Figure 10: All types of features of the Bot-IoT dataset arranged in descending order based on MI score. Figure 9: The confusion matrix of DNN-IDS methodology considering the different number of features. The confusion metrics of the different DL-based NIDS for IoT considering the features given in Table 6 are shown in Figure 12. It was observed that the categorical features have eventually improved the detection performance for all of the DL methodologies. The DNN and CNN-1D detected the anomaly and benign samples with almost 100% accuracy. The GRU, RNN, and LSTM all improved their detection performance, comparing the results obtained while experimenting using only the numerical feature set. We observed that LSTM, among the other considered DL-based methodologies, exhibited more incorrect predictions. We also observed that the DNN achieved this result in just two hidden layers, which have eventually reduced the model complexity. A 100% accuracy for the binary classification stage is also achieved by the authors in their proposed solution [68]. Our work is different from their work in many ways. Firstly, they adopted the ML approach, while we used the DL approach for this study. Secondly, they achieved 100% accuracy results considering the 20 features while we only needed five features. The DL approach is more suitable for an IoT network, as it has shown its superiority to process a huge amount of data to make efficient and correct predictions. The present study provides a detailed comparative analysis of different DL-based NIDS for an IoT network. The MI score is considered as the feature selection criteria. Only  
Figure 11: Comparison of Accuracy scores considering different features of different data types. Figure 12: The confusion matrix of DL-NIDS methodology considering the top 05 features with MI \(>\) 0.5.  
the numerical features were first arranged in descending order, and the DL models were evaluated considering the 80, 32, 16, and 8 feature sets. For these scenarios, DNN achieved the highest detection accuracy of 99.01 compared to the other DL methodologies. We then repeated the experiments that considered the numerical and categorical features. We consider only those features whose MI score was \(\geq\) 0.5, which resulted in only five features. The experimental results showed a significant improvement in terms of detection accuracy, with the DNN achieving 100% results. The present study only considered the binary classification to detect only the anomalies in general. The considered model is not able to identify the exact nature of the anomaly, which is required to design an intrusion prevention mechanism. In addition, the present study only evaluated considering IoT-Botnet 2020 dataset. The proposed methodology needed to be evaluated considering different other IoT-based datasets to check its effectiveness for the considered features set. Moreover, the evaluation of the current research work is performed using only the simulation environment. It should be tested in a real-time environment to check efficiently. The performance of the considered solution should be tested under a large number of IoT sensors.