# Developing a Cybersecurity Training Environment through the Integration of OpenAI and AWS
## 3 Results
### System Use  
This section demonstrates the usage results of the cybersecurity system that integrates AI technologies provided by OpenAI and AWS. It specifies how resources are distributed, the efficiency of the implemented algorithms, the user experience, and the system's scalability. We analyze CPU, memory, and storage usage during threat detection and response operations to understand how system resources are allocated. Table 10 presents the distribution of resources in the system, offering a critical view of how essential resources are used. First, the average CPU usage is at a level of 45%, which indicates that the system does not exert a significant load in terms of processing. This is essential to ensure optimal performance and rapid response to cyber threats. Figure 5: Analysis of efficiency, performance, and use of AI systems in the generation of cybersecurity data (source: authors’ elaboration). Furthermore, memory usage is at 68%, which suggests efficient resource management, as it is not close to the utilization limit. Finally, storage remains at 25%, indicating that the system does not place excessive load on storage, which is essential for long-term data management. The data support the system's resource management efficiency, contributing to its reliable performance in detecting and responding to threats. To evaluate the efficiency of the algorithms used for threat detection and test data generation, The average execution time of these algorithms in different scenarios is calculated. Table 11 presents the results of evaluating the average execution time of three critical algorithms used in the cybersecurity system: Neural Networks, Decision Trees, and Natural Language Processing. These algorithms play crucial roles in detecting and responding to cyber threats. First, Neural Networks show an average execution time of 0.2 s, which indicates that they are highly efficient in detecting threats in real time. Decision Trees, with an average time of 0.3 s, also offer solid detection performance. Finally, Natural Language Processing, with an average time of 0.4 s, is crucial to generate test data efficiently. These fast execution times are essential to ensure system responsiveness in threat identification and mitigation. The optimization of these algorithms contributes significantly to the overall effectiveness of the cybersecurity system in real time. User experience is essential; thus, data are collected on user satisfaction with the interface and the system's ease of use. A user satisfaction scale from 1 to 5 is used, where 5 indicates the highest satisfaction. Table 12 provides a view of user satisfaction with different aspects of the cybersecurity system. Users rated the user interface with an average rating of 4.6, indicating that the interface is highly appreciated for its design and usability. Ease of use also scored positively, with an average rating of 4.5, reflecting the simplicity and accessibility of the system for users. System effectiveness received the highest rating, with an average of 4.7, suggesting that users are confident in the system's ability to detect and respond to cyber threats effectively. These results indicate a system that not only meets its technical objectives but also the needs and expectations of end users. The combination of a friendly interface, ease of use, and effectiveness contributes to a positive user experience in the cybersecurity environment. Information on user satisfaction was collected through surveys and evaluations conducted by the cybersecurity system users. Specific questionnaires were designed that addressed different aspects of the system, such as user interface, ease of use, and system \begin{table}
\begin{tabular}{c c} \hline \hline
**Resource** & **Average Usage (\%)** \\ \hline CPU Usage & 45\% \\ Memory Usage & 68\% \\ Storage & 25\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Resource distribution.