## 1 Introduction  
The Internet of Things (IoT) can be defined as connected objects (electronic-based) linked through a network to communicate with each other [1]. The concept of IoT is not new; on the contrary, it comes from the late 1990s, when [PERSON], the co-founder of Auto-ID Center at MIT, referred to it in order to describe computers connected to individual objects [2]. These objects mostly consist of computing devices, digital and mechanical machines, or microchips implanted in living creatures that own unique identifiers (UIDs) [1]. However, the actual implementation of IoT took place between 2008 and 2009 according to Cisco Systems [3]. Since then, IoT has been improved and applied in a different number of applications, including motion detection [4], smart home [5], farming [6, 7], cities [8, 9], connected cars [10], wearable health monitors [11], biometric cybersecurity scanners [12], and logistics tracking [13]. In view of the rapid speed of evolving and existence of IoT devices, it becomes challenging to prevent all security attacks [14]. This occurs due to the fact that IoT devices are created without considering the security and privacy factors [15]. Therefore, such measures cause many vulnerabilities and threats for these devices; for example, in 2019, a team from Cable News Network (CNN) managed to access various types of camera feeds [16]. They did this using the IoT devices search engine (Shodan), in which several feeds were revealed. One of these feeds showed the daily routine of an Australian family, a man in Moscow getting ready for sleep, and a cat been feeding by her owner in Japan. Furthermore, all cameras did not have any security checks and could be accessed by anyone according to CNN. Another situation happened in 2017, where the US FDA stated that St. Jude Medical's implantable cardiac devices could possibly be hacked [17]. These devices are responsible for monitoring patients' heartbeat and checking for heart attacks. Consequently, hackers could control device functions such as drain the battery, incorrect pacing, and shocks. The huge data (big data) generated from IoT devices made the detection of such attacks nearly impossible without proper mechanisms. One of these mechanisms is the Intrusion Detection System (IDS), which is a defense system that is responsible for monitoring the activities of the network in IoT devices [18]. There are several intrusion detection approaches, such as preemptive blocking, signature-based, and anomaly detection. In recent years, anomaly-based intrusion detection has shown superior performance over the other approaches, especially using machine learning techniques [19]. The main idea of machine learning-based detection is to train a trustworthy activity model and compare it against the new behavior. Machine learning is known to have better generalization properties than other conventional methods, given the potential to train hardware and application configurations. Due to the big data generated from IoT devices, machine learning algorithms consider the optimal approach to deal with such data using their ability to deliver meaningful interpretations and predictions as well as deep analysis of the data patterns [20]. The authors of [21] stated that, to develop a computational approach that can detect different types of cyber-attacks, an intelligent data-driven intrusion detection system is required, by means of machine learning technique. [PERSON] et al. [22] reported that it can be simple to bypass the signature-based intrusion detection system if the the attack is modified slightly, whereas machine learning-based methods can detect these variations as a result of the learning characteristics of the activity of the traffic flow. Further, it can capture the complicated properties of such attacks through learning their behavior and improve the speed as well as the detection performance better than the traditional intrusion detection system.