###### Abstract  
The emergence of smart technologies and the wide adoption of the Internet of Things (IoT) have revolutionized various sectors, yet they have also introduced significant security challenges due to the extensive attack surface they present. In recent years, many efforts have been made to minimize the attack surface. However, most IoT devices are resource-constrained with limited processing power, memory storage, and energy sources. Such devices lack the sufficient means for running existing resource-hungry security solutions, which in turn makes it challenging to secure IoT networks from sophisticated attacks. Feature Selection (FS) approaches in Machine Learning enabled Intrusion Detection Systems (IDS) have gained considerable attention in recent years for having the potential to detect sophisticated cyber-attacks while adhering to the resource limitations issues in IoT networks. Apropos of that, several researchers proposed FS-enabled IDS for IoT networks with a focus on lightweight security solutions. This work presents a comprehensive study discussing FS-enabled lightweight IDS tailored for resource-constrained IoT devices, with a special focus on the emerging Ensemble Feature Selection (EFS) techniques, portraying a new direction for the research community to inspect. The research aims to pave the way for the effective design of futuristic FS/EFS-enabled lightweight IDS for IoT networks, addressing the critical need for robust security measures in the face of resource limitations. ensemble feature selection; feature selection; internet of things; intrusion detection system; lightweight +
Footnote †: journal:  
as well as social and business communities. IoT is estimated to have an overall economic growth of up to USD 1 trillion by 2025 [10]. Several businesses, including health sector and home appliance manufacturers, have already begun to see their turn in investment by empowering their products with IoT technologies [11]. However, IoT growth is affected by several major challenges, mainly the unprecedented large-scale deployments, heterogeneity of the end and gateway IoT devices, and the absence of securing solutions for resource-constrained IoT devices [12; 13; 14]. While the use of IoT devices can benefit many sectors, however, such a deployment also has the potential to become a major source of cyber-attacks [15; 16], where recent incidents show significant financial losses due to exploitable vulnerabilities in IoT devices [17; 18]. Security and privacy have always been key concerns in the IoT ecosystems, which subsequently yielded increasing research in these domains, including the design of intelligent Intrusion Detection Systems (IDS) [19; 20]. Recent attacks on IoT systems have significantly increased the importance of anomalous IDS, especially with increasing incidents of Distributed Denial of Service (DDoS) attacks [3]. Over time, the attack vectors have evolved in terms of complexity, diversity, and usage of various intelligent techniques, where malicious adversaries employ such techniques to launch attacks leading to information leakage, data loss, or even a complete system failure [21; 22]. Attack vectors are becoming intelligent to exploit the weak security mechanisms employed within the current environments. Consequently, Machine Learning (ML) algorithms have gained considerable attention as intelligent solutions for the detection of anomalous network packet streams and cyber-attacks [23]. Classification and clustering abilities of ML algorithms can empower anomaly detection models for the efficient detection of novel cyber-attacks, where various ML-based intrusion detection models have been adopted by the research community and have shown promising results [24; 25; 26; 27]. IoT devices are generally known to have fewer resources for employing conventional security solutions, whereas existing IDS techniques are resource-hungry, hence deeming them inappropriate for IoT networks [28].