# XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection
## 1 Introduction  
The Fourth Industrial Revolution will be fueled by cutting-edge and innovative technologies where the Internet of Things (IoT) devices will play integral roles [1]. Countries prepared to accept these changes will undoubtedly have a better chance of success in the near future. IoT is one of the fastest expanding fields in the history of technology, with around 50 billion devices in use by the end of 2020 [2]. As a result, the devices' security threats are increasing on a large scale. Because IoT devices lack fundamental security protocols, they have become tempting targets for attackers. According to recent estimates, IoT devices are subjected to an average of 5200 attacks per month [3]. Only in the first half of 2019, attacks against IoT devices tripled compared with the previous year [4]. According to Checkpoint's study, 71% of security experts have observed an increase in security risks in IoT networks after the prevalence of COVID-19 [5]. Recently, the Internet of Medical Things (IoMT) [6, 7], the Long Range (LoRa) IoT networks [8], Blockchain-based supply chain systems [9, 10] and Smart industries [11] have also been targeted by a huge number of attackers. Many of these attacks are extremely hazardous for the devices. Mirai and BASHLITE, found in 2016 and 2015, respectively, are two common IoT botnets thatinfected numerous IoT devices over the years [12]. Among them, the Mirai Distributed Denial-of-Service (DDoS) attack was the third most frequent IoT threat in 2018. Again, a total of 15.4 million DDoS attacks are expected worldwide by 2023 [13]. So, with the rise in cybercrime, detecting these attacks has become a crucial field of research [14]. There should be an effective framework assuring the security of IoT devices as these issues are highly alarming. Nonetheless, the use of regular security techniques for detecting botnet attacks would not be sufficient to ensure the safety of the systems. Many researchers have been working on developing intrusion detection systems in recent years, as the attacks increasingly target IoT devices. They mainly focus on two types of botnet attacks in the IoT environment, namely host-based [15], and network-based [16]. However, some of the common machine learning approaches for intrusion detection include support vector machine (SVM), K-nearest neighbor (KNN), neural networks, and Naive Bayes [17; 18; 19]. [PERSON] et al. [20] presented a methodology for selecting the efficient machine learning algorithm in IoT devices to detect malicious attacks. They claimed Naive Bayes as the efficient algorithm according to their experimental analysis. [PERSON] et al. [21] performed a feature selection approach on the N-BaIoT dataset to detect IoT attacks. They obtained nearly 99% accuracy using three distinct machine learning algorithms, including Naive Bayes, ANN, and J48 decision tree. [PERSON] et al. [22] introduced an intrusion detection method for IoT devices that uses deep learning as a primary tool for detection and achieved significant improvement. [PERSON] et al. [23] evaluated the NSL-KDD dataset for intrusion detection using random forest (RF), SVM, and extreme learning machines (ELM) algorithms. Their findings indicated that ELM outperformed SVM when dealing with large data. In another study, a network-based intrusion detection proposed by [PERSON] et al. [24] achieved a detection rate of 96.8% when utilizing K-means clustering after manually selecting 8 to 16 features. [PERSON] et al. [25] proposed an unsupervised model which is autonomous. This model is able to observe security issues in network environments where an ensemble autoencoder algorithm is used. This classification model, which operates on low computational resources, is primarily composed of two steps: offline training and online testing on IoT devices. In addition, [PERSON] et al.