# Integration of Machine Learning-Based Attack Detectors into Defensive Exercises of a 5G Cyber Range
## 1 Introduction
On the other hand hand, AI has proven to be an effective tool in helping organizations identify vulnerabilities that can be exploited in their cybersecurity infrastructure to gain a foothold in the network [2]. Hence, AI can also be used to enhance and support the training of cybersecurity professionals, who will be able to test their cybersecurity skills in simulations and receive more effective and faster training to react to and mitigate real-life cyberattack scenarios more effectively. In this way, AI can also support cybersecurity professionals outside the training environment in their daily activities. In this context, AI-based tools have a great potential to assist cybersecurity professionals and organizations in the ongoing development of effective countermeasures to defend their networks and data against the latest cyberattacks in this never-ending arms race. In addition to the above benefits, AI also has the potential to greatly accelerate the process of detecting and mitigating cyberattacks, enabling cybersecurity organizations to respond to these threats more efficiently and effectively. One of the major hurdles for cybersecurity is the time lag between the detection of a cybersecurity breach and the implementation of effective countermeasures. This is due, in part, to the limitations of human cognition and reasoning. In most cases, the process of finding a solution to a cybersecurity problem involves tedious research and it also takes considerable time to develop and implement the necessary software solutions. AI can greatly assist in these processes by acting as a \"search engine\" to speed up the process of finding the best solutions and shorten the time needed to detect and mitigate cyberattacks. Therefore, AI-based cybersecurity tools have the potential to significantly boost the cybersecurity performance of organizations and individuals across industries and business sectors. Furthermore, the use of AI methods presents great potential for identifying patterns in encrypted traffic that can be used to detect malicious activity, an application area where rule-based heuristic methods that power current Network Intrusion Detection Systems, such as Snort, have proven ineffective. The integration of ML models during cybersecurity operations has the potential to greatly reduce response times in the event of an attack and uncover a number of relevant cyberattacks that might otherwise have gone undetected, thus providing organizations with greater protection against cybersecurity threats than existing methods. Therefore, it is important to train cybersecurity personnel to be able to take advantage of these ML-based tools across a wide range of attacks, allowing them to accurately and quickly discover and identify cyberattack vectors in real-time, enabling faster development of effective countermeasures to attempt to mitigate the effects of the attack as quickly as possible. Moreover, as we noted earlier, an increasing number of cyberattacks are adopting ML-based methods to penetrate targeted systems with advanced adversarial example-based attacks that can circumvent the security of current cybersecurity systems [2]. For this reason, it is crucial to introduce new cybersecurity capabilities in cyber ranges that incorporate ML and AI into cybersecurity training procedures to ensure that cybersecurity professionals understand how ML is used in practice and how it can help detect and mitigate these advanced cyberattacks. Finally, 5G and beyond 5G widespread adoption and associated telecommunication technologies, such as NFV and SDN, are expected to pose new cybersecurity and privacy challenges. As telecom operators deploy 5G networks, cybersecurity professionals must become familiar with these new technologies and their underlying technical requirements and challenges to address the potential impacts of future cybersecurity threats that can target critical 5G infrastructure.