## 2 Related Work  
Significant and fruitful efforts have endeavored to address the security concerns of recent years for the IoT ecosystem. Several new IoT security technologies were established by pairing artificial intelligence techniques and cybersecurity virtues. Several promising state-of-the-art studies have been conducted for IoT security using machine learning (ML) and deep learning (DL) techniques [38; 39; 40; 41; 42; 43; 44; 45; 46; 47]. However, only a few were developed by investigating the impact of using different feature selection approaches to improve prediction and classification accuracy. For instance, [PERSON] et al. [11] have proposed and implemented a new minimized redundancy discriminative feature selection (MRD-FS) technique to resolve the issue of redundant features. The discriminating features have been selected based on two criteria, i.e., representativeness and redundancy. Their model was evaluated utilizing the BoT-IoT dataset. [PERSON] et al. [23] presented a flexible,mutual information-based feature selection technique (FMIFS) that chooses the best features to enhance the classification algorithm. The proposed model was evaluated using three datasets (NSL-KDD, KDD Cup 99, and Kyoto 2006). The Least Square Support Vector Machine-based IDS (LSSVM-IDS) was used to measure performance. [PERSON] et al. [23] showed 99.79% accuracy, 99.46% detection rate (DR), and 0.13% FPR over the KDD99 dataset. However, their employed datasets are not up-to-date (date back to 2009, 1999, and 2006 for NSL-KDD, KDD-Cup99, and Kyoto datasets, respectively) and do not fully represent the IoT cyberattacks. Similarly, [PERSON] et al. [24] proposed a modified mutual information-based feature selection technique (MMIFS) applied with the SVM to improve the accuracy performance of the classification and to (highly) efficiently detect the various attack types. They demonstrated how high data dimensionality could be enhanced using the feature selection technique. Note, high dimensionality, even if applied to a high-quality ML approach, produces poor detection rate and accuracy performance. MMIFS can reduce features to only eight features (out of 41). For instance, MMIFS with SVM using only eight features, and DR achieved 86.46%. In the first phase, data normalization and reduction are applied by dividing every attribute (feature) value by its maximum value. In the next phase, feature selection is applied based on the imported training data. Further, MMIFS initially takes the feature set as the empty set. In more detail, it calculates the mutual information of the features concerning the class target and then picks the first feature with the maximum mutual information value. Moreover, [PERSON] et al. [48] proposed an approach integrating k-nearest neighbors with the k-means algorithms (KNN) based on feature extraction to select the best features and classify network attack types. Two-dimensional vectors are created. In the first phase, a clustering algorithm is applied to cluster the training dataset, determining the new feature value based on two distances. The first is between a current feature and its cluster center (centroid), and the second is between the current feature and its nearest neighbor. A new one-dimensional distance based on feature value represents each feature (attribute) in the training dataset. In the next phase, principal component analysis (PCA) is applied to select the relevant features and omit irrelevant ones. In a similar context, [PERSON] et al. [49] introduced a wrapper technique for feature selection of their IDS. Their approach uses logistic regression and a genetic algorithm as an exhaustive search strategy for classification methods. Moreover, the decision tree (DT) classifier has been applied in their model, which has enhanced performance: an accuracy of 99.9% and false alarm rate (FAR) of 0.105% on the KDD Cup 99 dataset using only 18 features. They name their approach the genetic algorithm and logistic regression (GA-LR) wrapper approach.