###### Abstract
In addition, the recent advancements in ML-based security solutions, including those for IDS, are generally known to be resource-hungry and require computing components having sufficient processing capabilities, memory storage, and power sources, hence deeming such approaches unsuitable as well for resource-constrained IoT devices [29]. With the rapid spread of IoT devices, it is expected that an enormous amount of data traffic would be generated over the internet; hence, cyber-security tools, such as the IDS, will go through inspecting large amounts of network traffic. Such network traffic contains information that can be taken as features in ML-based classification models, but much of such information is usually not effective for the detection of an intrusion or tends to contain redundant flows. Having said that, classification models prepared based on all given network traffic would require heavy resources for their training, testing, and traffic handling. Moreover, such established models are inappropriate for devices that rely on embedded components that are inherently limited in processing power and memory storage. To address the above-described limitations and improve the efficiency of IDS over IoT devices, the notion of lightweight IDS was introduced in [30], where lightweight IDS are optimized intrusion detection approaches that are flexible enough to be deployed in computationally frail devices [15]. In the last few years, several research works have investigated the design of ML-based lightweight IDS for IoT systems, where researchers mostly adopted a Feature Selection (FS) approach to reduce the number of input features utilized for preparing the classification model, thereby resulting in an optimized but intelligent computing solution [31; 32; 33; 34]. Though the scientific community has largely contributed to this aspect of research, to the best of our knowledge, a comprehensive survey paper has not been presented in the literature yet that would address all relevant aspects of FS-enabled lightweight IDS for resource-constrained IoT devices. This paper presents a comprehensive survey covering key aspects of FS-enabled techniques used for designing lightweight IDS as security solutions suitable for the IoTecosystem. The survey provides a review with sufficient depth and breadth on ongoing research in IoT security, the resource-constraint nature of IoT devices, an exhibition of several real-world IoT products, and a projection of their limited resources. The discussion on real-world IoT products provides the reader with an actual sense of the limited resources issue in IoT end and gateway devices, along with the dire need for lightweight security solutions in such devices. The survey also discusses the widely adopted FS techniques used for building classification models as being the underlying approach for reducing resource consumption in IoT devices. Further, a discussion on popular datasets used in building lightweight classification models is presented. Adding to that, this study delivers a special direction toward the emerging Ensemble Feature Selection (EFS) techniques and their utilization for reducing resource consumption in IoT devices. To the best of our knowledge, this work depicts a unique review that comprehensively discusses all aspects concerning lightweight FS/EFS-based IDS for IoT-based systems, where other relevant surveys partially cover some of the aspects while leaving key aspects un-discussed. A summary of the contributions made in this work is given below:  
* Detailed review of relevant survey works and the motivation derived from the existing surveys. * A detailed outlook of IoT resource constraint nature, analysis of resource limitations in real-world IoT products, IoT layered architecture, and recent threats in corresponding layers. * Insights on significant feature selection techniques applied on distinct datasets as the mechanism to reduce resource requirements of classification models.