<p>In the IoT realm, an Intrusion Detection System (IDS) is crucial for ensuring the security of IoT ecosystems, which include diverse interconnected devices, sensors, and systems. Because of the dynamic characteristics inherent to these ecosystems, they are vulnerable to a range of security risks. The Intrusion Detection System (IDS) is pivotal in its function to monitor and detect any anomalous activities within this intricate and constantly evolving environment. This paper has delved into the framework of Feature Selection (FS) and various FS models, providing detailed insights. The scope of this paper extends to a comprehensive review on FS in the context of IoT Intrusion Detection Systems (IDS). The principal aim is to grasp the fundamental concepts inherent in different FS approaches and identify the central notion of how FS can effectively trim down features for IoT IDS, thereby enhancing its lightweight characteristics. The review offers essential guidance for both researchers and practitioners aiming to implement robust feature selection techniques in IoT security, providing valuable perspectives and practical recommendations. In addition to reviewing existing FS approaches, this paper also proposes a novel FS technique that is adaptive, lightweight, and capable of considering inter-feature relationships. This technique overcomes the limitations of traditional FS methods, which often fail to address the dynamic nature of IoT environments or the complex interdependencies between features.</p>