# Integration of Machine Learning-Based Attack Detectors into Defensive Exercises of a 5G Cyber Range
## 1 Introduction  
The increasing complexity of cybersecurity threats, combined with the rapid development of technological advances, such as 5G and the Internet of Things (IoT), has increased the need for highly skilled cybersecurity professionals in various industries, especially in fields where data sensitivity and infrastructure security are paramount, such as financial institutions, transportation systems, energy systems, military, and healthcare systems, among others. To respond to these challenges, organizations and public administrations increasingly need their personnel to acquire more advanced cybersecurity skills to cope with the relentless development of new cybersecurity attack strategies, and to be fullyprepared to deal with these attack scenarios when they occur. In addition, cybersecurity training programs must develop new skills and curricula to ensure that personnel are trained to be able to respond effectively to threats at all levels, with a special emphasis on the operational and tactical levels. To achieve this goal, it is necessary to educate cybersecurity workers with innovative learning resources that provide them with a comprehensive understanding of the security issues involved in adopting new technologies, as well as to give them access to training scenarios that realistically represent the situations that may occur in their organization, while providing them with the facilities to practice their skills and develop and test preventive measures and countermeasures for these attacks in an isolated, dynamic environment that can be easily adapted to meet the stringent demands of this rapidly evolving landscape. The use of cybersecurity simulation environments, also known as cyber ranges, is one of the ways organizations can improve the cybersecurity awareness and capability of their security personnel while keeping costs low. A cyber range is a controlled environment in which cybersecurity professionals, students, or other participants learn how to react to and mitigate cyber-attack simulations in a safe, flexible, and scalable manner. Cyber ranges are based on realistic scenarios that model real-world cyberattacks based on typical attack vectors and require cybersecurity professionals to interact with the environment and use their skills to solve the problems presented. In this way, cybersecurity professionals can acquire and practice cybersecurity skills in these scenarios while learning to react to, mitigate, and, most importantly, prevent these cyber attacks in a timely and efficient manner. The use of cyber ranges is not only highly effective in improving the cybersecurity capabilities of the different roles and employees that collaborate on security-related tasks but also helps increase the confidence levels of the personnel and enhance their situational awareness to face attack situations in a more effective manner. To acquire the skills needed to successfully overcome cyberattacks, it is not enough for cybersecurity workers to learn the theoretical and scientific aspects of these attacks. Instead, cybersecurity professionals must acquire a deep working knowledge of how to deal with, identify, and mitigate cyberattacks. To achieve this, they need to better understand how cyber terminals think and plan, as well as create countermeasures to defend their networks and data. The cybersecurity field in particular, as well as other business fields, is increasingly relying on Artificial Intelligence (AI), which plays a key role in helping cybersecurity professionals detect, investigate, mitigate, and prevent cyberattacks. On the one hand, AI can be used to improve existing cybersecurity processes with passive detection. For example, AI-based software has been used to help organizations detect signs of an impending cyberattack, such as detecting suspicious activity and network intrusions, and develop protective measures to prevent their occurrence or countermeasures to mitigate their effects [1].