# A Survey on Botnets, Issues, Threats, Methods, Detection and Prevention
## 1 Introduction  
Botnets are tools used to deliver malware across networks. This defines them as major threats to corporate networks, which may always need data, applications and services available. Botnets are the primary cause of Denial-of-Service (DOS) attacks. They can prevent employees from accessing (sensitive and confidential) information stored within the networks. It may negatively affect the data's availability on the system and the data-handling credibility of the data handler. Despite how common DOS attacks are, businesses may not have any solutions to combat this threat. A well-known example of a botnet attack is the Mirai botnet attack in 2016 [1]. The Mirai bot-master (i.e., the malware owner) conducted reconnaissance of unsecured Internet of Things (IoT) devices that were connected to the target networks and then successfully launched the malware against unsecured ports. The Mirai botnet successfully accessed target devices due to poor IoT security configuration, e.g., with weak passwords being assigned. Also, the malware used brute-forced password cracking to access IoT devices. Kraken, another botnet attack that occurred in 2008, infected over 400,000 personal computers (PCs) and was predominantly used for spamming other network users to gain system access or cause system damage [2]. The number of botnetattacks is rapidly increasing with many new variants based on more advanced techniques which can't be easily defended using traditional and old defence strategies. Thus, mitigating botnets and providing businesses with the best solution to respond to the large number of various threats that may exploit them is becoming more challenging. AI is known to assist with the improvement of network security due to its decision-making capabilities. However, also, AI can be used by attackers to launch more sophisticated botnet attacks. This highlights the importance of AI to be considered as key enabler to design and develop more efficient botnet detection and prevention tools and strategies compared to the old methods. For example, AI-based methods are particularly necessary for IoT networks. Their in-built security may not be updated and able to counter botnet threats, since threat actors can create botnet code variations to bypass certain firewalls, allowing them unauthorised access to hosts. It is therefore important to consider what methods can be used to prevent and mitigate botnet attacks. This paper provides a review on current and past methods for botnet infections, followed by discussing traditional and modern AI-based methods and strategies for botnet detection and prevention.  
The remainder of this paper is organized as follows: Section 2 discusses what a botnet is, along with what its capabilities are when it has successfully infected a target host. Section 3 explains the various methodologies bot-masters may use to infect hosts, either through code variation or vulnerability exploitation. Section 4 details how botnets can be detected by the system along with how AI and Machine Learning algorithms may contribute to botnet detection. Section 5 discusses prevention and countermeasures that businesses and individuals can use to avoid botnet-related threats. Section 6 discusses and evaluates the efficiency of some well-known Machine Learning algorithms for botnet detection. Finally, Section 7 provides an overall conclusion.