## 3 Proposed Anomaly-Based IDS for IoT Ecosystem  
Identifying and selecting relevant features in the dataset has become crucial to improving ML model performance, especially for anomaly-based IDS. To address the challenge of improving an anomaly-based IDS in the IoT ecosystem, we deal with node data attributes to identify relevant and redundant features. Redundant features affect the models' performance and make those models less reliable [11; 28; 32]. We assume that the current FS techniques do not always guarantee the best relevant features or eliminate redundant \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Reference** & **Feature Selection Approach** & **Number of Features** & **Detection Model** & **Environment** & **Datasets** & **Performance Metrics** \\ \hline
[23] & FMIFS & Candidate Features Selected & LS-SVM & IoT system & KDD Cup 99 d, NSL-KDD, Kyoto & DR, FPR, Accuracy \\ \hline
[24] & MMIFS & Candidate Features Selected & SVM & Discrete, Continuous environment & KDD Cup 99 &
\begin{tabular}{c} Falsepositive rate \\ (FPR), false detection rate (DR), Accuracy \\ \end{tabular} \\ \hline
[25] & - & Candidate Features Selected & SVM, ANN & Continuous environment & DARPA &
\begin{tabular}{c} Accuracy, False \\ positive rate, False \\ negative rate \\ \end{tabular} \\ \hline
[32] & mRMR, wrappers, Max, MaxRel & Candidate Features Selected & NB, SVM, LDA &
\begin{tabular}{c} Discrete and \\ continuous \\ environment \\ \end{tabular} & HDR, ARR, NCI, LYM & Lowest Error Rate \\ \hline
[48] & PCA & 6 and 19 & KNN with K-means, CANN & \
\begin{tabular}{c} Continuous \\ environment \\ \end{tabular} & KDD Cup 99 &
\begin{tabular}{c} Accuracy, Detection \\ rate, False alarm \\ \end{tabular} \\ \hline
[49] & Wrapper, GA, LR & \begin{tabular}{c} 15, 16, \\ 17, 18, \\ 20, 22 \\ \end{tabular} & \
\begin{tabular}{c} logistic regression, a \\ genetic algorithm, \\ decision tree \\ \end{tabular} & IoT system & KDD Cup 99 &
\begin{tabular}{c} Accuracy, Recall, \\ DR, FAR \\ \end{tabular} \\ \hline
[50] & Correlation (CR), (IG) & 25 & ANN & IoT system & NSL-KDD &
\begin{tabular}{c} Confusion matrix, \\ Recall, Precision, \\ FPR, TPR, Accuracy \\ \end{tabular} \\ \hline
[51] & Wrapper, GA & \begin{tabular}{c} 11, 9, 16, \\ 20, 22, 36, \\ 34, 41 \\ \end{tabular} & \
\begin{tabular}{c} random tree, \\ decision stump, \\ naive Bayes, C4.5, random forest, and \\ representative tree \\ \end{tabular} & Continuous environment & KDD Cup 99 &
\begin{tabular}{c} TP Rate, FP Rate, \\ Precision, Recall, \\ F-Measure \\ \end{tabular} \\ \hline
[52] & Wrapper, RHMC &
\begin{tabular}{c} Candidate \\ Features Selected \\ \end{tabular} & SVM & IoT system & KDD Cup 99 & False positive rate \\ \hline
[53] & Shapiro-Wilk & \begin{tabular}{c} Candidate \\ Features Selected \\ \end{tabular} & \
\begin{tabular}{c} SVM, Gaussian NB, \\ LDA, LR, Descion \\ Tree, Random forest (RF), Ensemble \\ \end{tabular} & IoT system & IoTID20 &
\begin{tabular}{c} Accuracy, presion, \\ Recall, F score \\ \end{tabular} \\ \hline
[54] & - & - & - & \
\begin{tabular}{c} SVM, Gradient \\ Descent (SGD), LR, \\ NB, SLFN, \\ oversampling \\ \end{tabular} & IoT system & IoTID20 &
\begin{tabular}{c} Accuracy, Precision, \\ Recall, G-mean \\ \end{tabular} \\ \hline
[55] & - & - & lightGBM, OASW & IoT system & IoTID20, NSL-KDD &
\begin{tabular}{c} Accuracy, Precision, \\ Recall \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of different feature selection approaches. features. Therefore, we've aimed to find new strategies in dealing with the discovery of useful features and concurrently the removal of unsupportive features. Existing methods may be easily implemented in the practical sense, but at the same time, they can and do consume lots of resources.