# Enhancing IoT Network Security Using Feature Selection for Intrusion Detection Systems
## 1 Introduction  
Rapid growth in the Internet of Things (IoT) has enabled automation and an unparalleled degree of interconnection and real-time analysis of disparate data, driving a revolution in several industries such as manufacturing, transportation, healthcare, and smart homes [1]. Intelligent sensing has been embedded into daily objects, and the efficiency of its application has been enhanced through IoT devices. Although IoT devices have become an important component of system infrastructure, a high level of connectivity introduces several security challenges because the system is then exposed to various risks. Owing to their deployment in insecure environments, IoT devices may be exposed to unauthorized access and several types of attacks, such as denial-of-service attacks [2, 3]. Generally, traditional measures of security demand a high level of resources, but IoT devices have a limited level of computational resources; thus, these measures cannot be implemented effectively [4]. Both IoT devices and the networks in which they are enrolled must be protected to maintain the continuously growing reliance on IoT systems. A lack of security may have severe effects on data privacy, physical safety, and operational continuity [5]. To avoid the negative consequences resulting from a lack of security, a robust, scalable, and efficient security solution is required [5]. IoT devices are often integrated into critical infrastructure and are deployed in remote and insecure environments. Therefore, they are exposed to significant security risks. Owing to limitations in memory, computational power, and energy, traditional measures of security, such as antivirus programs and firewalls, are not effective [6]. Moreover, the lack of homogeneity in IoT systems further complicates the development of useful security systems [7], and the vulnerabilities in these systems expose them to attackers. Considering various entry points, attackers can exploit IoT devices and infiltrate networks, potentially resulting in large-scale cyberattacks. With the current increase in the sophistication and level of cyber threats, security solutions must be tailored to protect IoT devices without stretching the limited availability of resources [8]. Overall, it is necessary to ensure the resilience and security of IoT systems. Intrusion detection is an important component of IoT security that is designed to effectively identify and proactively mitigate potential threats and damage. Traditional methods rely on the predefined signatures of known attacks. Conversely, anomaly-based intrusion detection systems (IDSs) are more relevant for IoT systems. These systems detect both known and emerging threats by monitoring the real-time behavior of the network and flagging any deviations from established norms [9]. The dynamic and diverse operation of IoT ecosystems can be managed by anomaly-based IDSs, thereby generating a flexible and robust defense system and ultimately preventing the exploitation of the unique characteristics of IoT devices. Apart from identifying attack signatures, these systems monitor activity and focus on detecting unusual patterns. This generates an important layer of protection, thus complementing existing security systems and securing IoT networks from the growing range and risk level of cyber threats [10]. The RT-IoT2022 dataset used in this study contains a large number of instances with a variety of features related to network behavior [11]. Larger and more complex datasets may provide more valuable results regarding intrusion detection. However, with major increases in the datasets generated by IoT devices, the usual process of analyzing data has become a complicated task for investigators. Hence, there have been continuing efforts to adopt machine learning methodologies to investigate more complex security concerns. In the present paper, we aim to facilitate research in intrusion prediction using a predictive model.