# Enhancing IoT Network Security Using Feature Selection for Intrusion Detection Systems
## 4 Results
Using a reduced set of features derived from the combination of five feature selection methods achieved the highest performance results of 96.4%, 97.4%, 87.1%, and 91.9% for accuracy, precision, recall, and F1-score, respectively, indicating that combining methods can effectively identify the most relevant features. It was observed that the percentage of runtime when using feature selection decreased by 66.4%. Among \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Number of Features** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Original data & 83 & 93.5\% & 61.7\% & 99.7\% & 76.2\% \\ \hline All FS methods & 16 & 96.4\% & 97.4\% & 87.1\% & 91.9\% \\ \hline CFS & 5 & 93.1\% & 80.9\% & 63.0\% & 70.8\% \\ \hline Pearson’s analysis & 32 & 94.8\% & 76.8\% & 99.1\% & 86.5\% \\ \hline Gain ratio & 51 & 96.0\% & 84.0\% & 90.3\% & 87.0\% \\ \hline Information gain & 45 & 95.1\% & 92.9\% & 89.9\% & 91.4\% \\ \hline Symmetrical uncertainty & 60 & 95.6\% & 93.9\% & 93.9\% & 93.9\% \\ \hline \hline \end{tabular}
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Table 4: Comparison of the performance of the MLP classifier using the full features dataset and different reduced feature datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Feature** & **CFS** & **Pearson’s Analysis** & **Gain Ratio** & **Information Gain** & **Symmetric Uncertainty** & **Number of Occurrences** \\ \hline fwd\_init\_window\_size & 1 & 1 & 1 & 1 & 1 & 5 \\ \hline bwd\_pkts\_payload.avg & 0 & 1 & 1 & 1 & 1 & 4 \\ \hline bwd\_pkts\_payload.max & 0 & 1 & 1 & 1 & 1 & 4 \\ \hline bwd\_pkts\_payload.std & 0 & 1 & 1 & 1 & 1 & 4 \\ \hline flow\_SYN\_flag\_count & 0 & 1 & 1 & 1 & 1 & 4 \\ \hline flow\_iat\_std & 0 & 1 & 1 & 1 & 1 & 4 \\ \hline flow\_pkts\_payload.max & 0 & 1 & 1 & 1 & 1 & 4 \\ \hline fwd\_iat\_avg & 0 & 1 & 1 & 1 & 1 & 4 \\ \hline fwd\_iat\_max & 0 & 1 & 1 & 1 & 1 & 4 \\ \hline fwd\_last\_window\_size & 1 & 0 & 1 & 1 & 1 & 4 \\ \hline fwd\_pkts\_payload.avg & 0 & 1 & 1 & 1 & 1 & 4 \\ \hline fwd\_pkts\_payload.max & 1 & 0 & 1 & 1 & 1 & 4 \\ \hline fwd\_subflow\_pkts & 0 & 1 & 1 & 1 & 1 & 4 \\ \hline id.resp\_p & 0 & 1 & 1 & 1 & 1 & 4 \\ \hline payload\_bytes\_per\_second & 0 & 1 & 1 & 1 & 1 & 4 \\ \hline service & 0 & 1 & 1 & 1 & 1 & 4 \\ \hline \hline \end{tabular}
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Table 3: The occurrence of the most significant features. A value of ’1’ means that the feature appeared in the specified feature selection method, while a value of ’0’ means that the feature did not appear. the individual methods, the gain ratio produced the highest accuracy at 95.96%, followed closely by symmetrical uncertainty (95.6%) and information gain (95.12%). Pearson's analysis and CFS yielded accuracies of 94.84% and 93.1%, respectively, showing a varied performance among the methods. In contrast, using the full set of features resulted in a lower accuracy of 93.48%, highlighting the importance of feature selection in improving classification performance and reducing noise. These findings demonstrate that combining feature selection techniques can outperform both individual feature selection methods and the use of the full dataset.