# Mitigation against DDoS Attacks on an IoT-Based Production Line Using Machine Learning
## 1 Introduction  
Cyberattacks occur in all areas of society and distributed denial-of-service (DDoS) attacks are among the most popular kinds and can overwhelm a network to the point of inoperability [1]. In a DDoS attack, the target machine or network is flooded with a number of redundant requests from a number of different sources. A DDoS attack can be a worry for various services related to digital infrastructure, such as banks, cloud services, Wikipedia, and political parties, but also households, hospitals, and industrial plants. Cybersecurity has become an increasing problem in industry as well.  
Technological developments in recent years have made it possible to connect a variety of devices to computer networks, which brings various benefits to users. However, with the rise of the technologies involved, the number of cyberattacks is also increasing, using more sophisticated means to incorrectly access sensitive information and to extort money or the already mentioned interruption of services. One such technology is the Internet of Things (IoT) [2].  
The concept of the Internet of Things includes various devices, sensors, objects, and intelligent nodes that are able to function autonomously and communicate with each other without human intervention. Such IoT devices are able to provide a number of useful services and, thanks to sensors and actuators, provide various data in real-time. In many cases, however, devices in the field of the Internet of Things, in particular, contain various software bugs brought in from the factory that make them vulnerable. Such vulnerabilities often allow attackers to perform various cyberattacks and compromise the security of the environment in which IoT devices are located [3].  
IoT devices have also found application in \"Industry 4.0,\" which seeks to increase efficiency in the industry through the collection, exchange, and analysis of information throughout the product life cycle. Intelligent production on the production line uses IoT as one of the important basic technologies and accelerates the modernization of traditional industrial production lines [4; 5]. However, applying IoT devices to the production line can lead to various cyberattacks, including a DDoS attack.  
Several defense mechanisms have been proposed in the past against DDoS attacks in IoT networks. They can be divided into two basic groups: traditional DDoS defenses and IoT-specific DDoS defenses. They differ in terms of location and complexity. While traditional DDoS defenses are applied to the target server and are essentially homogeneous, IoT-specific DDoS defenses are applied to IoT devices and are more complex, reflecting the heterogeneity of IoT devices. In both cases, detection techniques are used to detect abnormal activities in the network or host. The taxonomy of DDoS defense mechanisms in the IoT network is described in detail in [1].  
One of the possible techniques used in defense against DDoS attacks is machine learning. Network and system security have long used machine learning to detect malware, anomalies, and intrusions [6].  
Academic institutions and industry segments are trying to find the optimal solution to the problem of DDoS attacks. Finding a balance between academic design and industry practice to combat DDoS is a major challenge. Although both sectors are making great efforts to combat DDoS attacks, DDoS incidents occur every day, emphasizing that the problem is not resolved [7].  
The production line is a specific production environment and requires a special approach to the problems of DDoS attacks and countermeasures when implementing cybersecurity, especially if it has integrated IoT devices.