## 8 Conclusions  
This survey provides in-depth insights into cutting-edge techniques adopted for designing feature selection/ensemble feature selection-based lightweight IDS for the IoT ecosystems. The study highlights how the widespread deployment of IoT devices into various application domains can provide ease to users. However, it also highlights how the security of such devices has become one of the prime concerns of the research community and manufacturers alike. Exploitation of IoT devices may constitute the world's largest cyber-attack surface, resulting in network assaults and illegal activities. A detailed discussion on IoT devices and their natural inclination towards being resource-constrained is presented, along with exhibiting the resources within several existing industrial products currently being adopted over different application domains. The issue of how conventional IDS solutions require heavy resources is put into argument while taking into account the limited resources within IoT end devices or gateways. The notion of lightweight IDS solutions for IoT environments is deliberated along with providing a detailed discussion of existing works in this specific area of research. It is deduced that three major approaches are being adopted by the research community to design effective lightweight IDS solutions, namely feature selection and emerging ensemble feature selection, both utilized through ML algorithms for building classification models, and finally the collaborative approach. It is also inferred that most of the adoption for building lightweight IDS is towards feature selection approaches with an inclination trend towards ensemble feature selection techniques that are further used to build classification models through different ML algorithms. In contrast, few adoptions of the collaborative approach are observed, as this approach requires effective communication channels to securely collaborate with edge, fog, and cloud layers, hence further increasing the complexity of security. While considering the feature selection approach, a detailed discussion is given on the potential datasets that are frequently adopted by the research community to design suitable classification models. Emphasis is put on recently developed datasets, including TON_IoT and BoT-IoT, that were established by collected records from an IoT environment, which qualifies them as more appropriate datasets for developing FS-enabled lightweight IDS solutions. The study also reveals the adoption of the emerging ensemble feature selection approaches for building lightweight IDS solutions for IoT devices as the future direction of research works in this area.  
It is observed that most of the proposed solutions were not tested in real-world IoT environments; therefore, the evaluation of IDS performance upon real-time generated network packets was lacking in such studies. Such an approach may result in classification models prepared out of different ML algorithms suffering from overfitting and poor generalization problems when exposed to data drawn from real-world networks. Hence, this area requires further investigation and proper attention from the research community while designing security solutions for IoT systems.  
Conceptualization, M.F. and O.R.; methodology, M.F. and O.R.; investigation, M.F., O.R. and I.M.H.R.; writing--original draft preparation, M.F.; writing--review and editing, M.F., O.R., I.M.H.R., A.A. and S.J.P.; supervision, O.R. and I.M.H.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.