# Intelligent Detection of IoT Botnets Using Machine Learning and Deep Learning
## 1 Introduction  
At the 2016 World Economic Forum (WEF, also known as the Davos Forum), The Fourth Industrial Revolution by [PERSON] became a turning point in transforming our society from an information society into an intelligent information society. The Fourth Industrial Revolution represents a fundamental change in the way we live, work, and relate to one another [1]. Key technologies leading the fourth industrial revolution include the Internet of Things (IoT), the cloud, big data, mobile technology, and artificial intelligence (AI). These intelligent information technologies are creating new industries and revolutionizing the ecosystem of existing manufacturing industries. The term \"Internet of Things\" was coined in 1999 by [PERSON] to describe how data collection through sensor technology has unlimited potential [2]. With the inclusion of the IoT in the Gartner Top 10 Strategic Technology Trends in 2020, it was shown that the IoT will develop into more than 20 times more smart devices than existing IT roles in 2023 [3]. According to Gartner, the overall usage of IoT in various areas, such as utilities, healthcare, the government, physical security, and vehicles, is expected to increase [4].  
As the IoT develops, cyber threats targeting IoT devices are also increasing. Most IoT devices are connected to the internet, which facilitates abuse and a lack of security control. The fact that IoT manufacturers failed to implement proper security controls to protect these types of devices fromremote attacks allowed the number of IoT attacks to increase during last year by 217.5%, up from the 10.3 million attacks logged by SonicWall in 2017, according to its 2019 Cyber Threat Report [5].  
There are many security threats targeting the IoT, which has many vulnerabilities. Because the IoT is subject to many threats, it is important to classify the relevant vulnerabilities and attacks in order to study the IoT. Some studies classify such attacks based on the IoT layer [6,7], the attacks themselves [8, 9, 10], and the vulnerabilities that can lead to the attack [11, 12, 13]. Through these studies, we found that jamming, DoS, man in the middle, routing, sinkhole, wormhole, flooding, virus, and worm attacks are the most likely to occur in an IoT environment. In particular, flooding and DoS attacks occur in production IoT environments through botnets.  
Botnet attacks [14, 15, 16], according to [PERSON], Mirai, and Bashlite, are especially surging in popularity. A botnet runs a bot on several devices connected to the internet to form a botnet controlled by the command and control (C&C) [17]. The botnet causes various types of damage, such as resource depletion and service disruption. AI is now widely used to detect these IoT attacks [18, 19, 20, 21].  
Currently, the IoT is attacked through various channels and methods. However, it is difficult to introduce security solutions and determine instances of hacking on the IoT through an analysis of security threats using network information. There are many recent security issues related to the IoT, which have increased awareness of such problems. Currently, research on IoT security threats is focused on analyzing and responding to networks [22, 23], but there are limitations that preclude detecting direct changes in hardware. Thus, unlike previous studies, we focused on attacks targeting IoT devices. In particular, we used deep learning and machine learning algorithms to detect such attacks efficiently.  
The remainder of this paper is organized as it follows. We briefly review the trends of IoT security threats and deep learning studies used in IoT security in Section 2. We design an IoT attack detection model based on five ML algorithms and three DL algorithms in Sections 3 and 4. Finally, the conclusions are presented in Section 5.