# Developing a Cybersecurity Training Environment through the Integration of OpenAI and AWS
## 3 Results
### Threat Simulation Results
Successful response was achieved in 95.35% of cases. The SQL Injection simulations involved 800 attempts. Of these, 780 attempts were successfully detected, resulting in a detection rate of 97.50%. A successful response was obtained in 96.15% of the cases. In the Cross-site Scripting (XSS) attack simulations, 800 attempts were made. Of these, 740 attempts were successfully detected and blocked, resulting in a detection rate of 92.50%. Successful response was achieved in 94.59% of cases. These results highlight the effectiveness of detection and response tools deployed in our cybersecurity environment, supporting the importance of robust measures in place to protect systems and data against various cyber threats. From the recorded data, the detection and successful response rates for each type of threat are calculated. These rates are expressed as percentages and reflect the system's performance in identifying and mitigating hazards. The results are detailed in Table 2, which shows the detection and successful response rates for each type of threat evaluated. The results obtained from the table reveal the strong performance of our cybersecurity system in detecting and responding to various cyber threats. Overall, successful detection rates range between 94.3% and 98.5%, indicating that the system is highly effective in the early identification of threats. This is critical to preventing cyberattacks before they cause significant damage. Additionally, successful response rates, ranging from 95.8% to 97.2%, reflect the system's ability to take effective action once a threat is detected. These values indicate that the implemented responses successfully mitigate the attacks, minimizing the potential impact on the organization's infrastructure and data. Importantly, these results are the product of the successful integration of AI technologies, such as those provided by OpenAI and AWS, which enable faster and more accurate detection and response. These results support the effectiveness of our approach to cybersecurity and demonstrate the importance of using advanced tools to protect organizations' digital assets in an increasingly threatening environment. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Threat Type** & **Detected** & **Detected** & **Total Attempts** & **Detection Rate** & **Successful Response** & **Successful Response** \\  & **Attempts** & **Attempts** & **(\%)** & **Response** & **Rate (\%)** \\ \hline DDoS attack & 950 & 950 & 1000 & 95.00\% & 900 & 94.74\% \\ Identity fraud & 880 & 880 & 900 & 97.78\% & 800 & 90.91\% \\ Malware & 860 & 860 & 900 & 95.56\% & 820 & 95.35\% \\ SQL injection & 780 & 780 & 800 & 97.50\% & 750 & 96.15\% \\ Cross-site scripting & 740 & 740 & 800 & 92.50\% & 700 & 94.59\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of detection and response rates by threat type. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Threat Type** & **Successful Detection Rate (\%)** & **Successful Response Rate (\%)** \\ \hline DDoS attacks & 98.5 & 97.2 \\ Identity fraud & 95.7 & 96.4 \\ Malware & 97.1 & 95.8 \\ SQL injection & 94.3 & 96.7 \\ XSS attacks & 96.8 & 97.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Calculation of detection and successful response rates by type of threat evaluated. Table 3 presents the performance metrics in four epochs, each covering a specific period. Time 1 corresponds to the beginning of the project, covering the first week of testing. Epoch 2 extends from the second week to the fourth week. Epoch 3 covers the fifth week to the eighth week, while Epoch 4 covers the ninth week to the end of the testing period. The division into epochs allows evaluation of how the system's performance evolves and responds to different loads and conditions at each project stage. The average of the metrics provides an overview of system performance over the entire testing period, and individual metrics at each epoch help identify significant changes and trends.