## 3 Existing Surveys and Motivations  
This section presents an overview of the surveys related to ML-enabled IDS for the IoT environment, while especially taking into account the adoption of FS techniques in ML-enabled IDS. [PERSON] et al. [35] provided a brief overview of streaming data learning issues, attack detection, and identification in IoT systems. In addition, the survey focused on the issue of IDS from three perspectives, including concept drift, high dimensionality, and computational complexity. It also discussed the computational load of some approaches and their effects on real-world systems. However, the studies related to high dimensionality and computational complexity were discussed in a limited form, whereas the datasets section only discussed bibliometric studies without any technical details. In [36], the authors presented a preliminary discussion on IDS, IoT, and ML techniques, along with identifying DT and Artificial Neural Network (ANN) as the most widely used ML algorithms. However, no technical justifications regarding the popularity of these techniques were discussed. Another survey [37] by [PERSON] et al. presented a comprehensive review that mainly focused on three domains, including IoT, IDS, and ML, where discussions related to recent intrusion detection approaches, IoT layers, protocols, and their security issues were presented. However, limited discussion on ML algorithms and literature related to ML-enabled IDS were presented. [PERSON] et al. [38] presented a comprehensive, in-depth analysis of various ML-based network IDS with a brief insight into various attacks and anomalies and their detection using IDS. Though the article covered multiple facets of ML areas, no discussion was presented related to the datasets used for developing the classification models. [PERSON] and [PERSON] published a survey article in [39] that is dedicated to delivering a comprehensive review of current IDS within the IoT landscape. It aims to provide an extensive overview encompassing IDS techniques, deployment strategies, validation approaches, and commonly employed datasets crucial for the development of IDS. All theoretical discussions are admirable. However, statistical results or experimental studies between different domains are not discussed. [PERSON] and [PERSON] [40] presented a survey that addressed issues related to the constrained nature of IoT devices and scalability issues in IoT systems. Furthermore, the authors discuss various intrusions in IoT, including physical, network, software, and encryption, as well as ML solutions to protect against these intrusions. However, it did not provide any discussion on the datasets usable for classification model development. In [41], [PERSON] et al. described the details of the IoT domain in terms of architecture, protocols, and attacks at each layer. Furthermore, it provided a systematic study of three technologies, including ML, AI, and blockchain. The critical analysis section of this paper discusses very generic issues regarding the selected technologies. However, various other factors are missing (i.e., real-time testing and retraining of ML and AI models, limitations of ML and AI algorithms, and trust issues in blockchain). [PERSON] et al. [42] explained an overview regarding the classification of IDS, different anomaly detection techniques, and different IDS models based on datasets. Various ML and Deep Learning (DL) techniques for data pre-processing and malware detection have been discussed. In addition, a brief overview of datasets has been provided. [PERSON] et al. [43] presented a survey on technologies related to IoT systems, protocols, architecture, and threats, along with providing an overview of IDS. In addition, it provides an analysis of various ML and DL techniques to detect attacks in IoT networks. However, the significance of a comparative study between ML and DL techniques does not reveal any results. In [13], [PERSON] et al.