# Developing a Cybersecurity Training Environment through the Integration of OpenAI and AWS
## 3 Results
### Effectiveness of AI in Data Generation and Analysis  
The results of the effectiveness of AI in data generation and analysis focus on evaluating the role of OpenAI AI tools, such as GPT-3 and DALL-E, in the generation and analysis of test data, as well as improving the capabilities of the cybersecurity training environment. One of the critical contributions of AI in this work is the generation of realistic and varied test data. GPT-3 created threat descriptions and cybersecurity scenarios efficiently and effectively. This enabled a diverse test data set covering many scenarios, from phishing \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Epoch** &
\begin{tabular}{c} **Existing Functionality** \\ **(Success)** \\ \end{tabular} & **New Updates (Success)** & **Observations** \\ \hline \hline \multirow{3}{*}{**Epoch 1**} & \multirow{3}{*}{95\%} & \multirow{3}{*}{94\%} & The existing functionality remains stable, with 95\% \\  & & & success. The new updates are 94\% successful, with \\  & & & some minor issues identified. \\ \cline{1-1}  & & & The existing functionality remains strong, with 94\% \\ \cline{1-1}  & & & success. The new updates have a success rate of 93\%, \\ \cline{1-1}  & & & with improvements in the identified incidents. \\ \cline{1-1}  & & & The existing functionality remains strong, with 96\% \\ \cline{1-1}  & & & success. The new updates have a 95\% success rate, \\ \cline{1-1}  & & & with fewer incidents. \\ \cline{1-1}  & & & The existing functionality remains robust, with 97\% \\ \cline{1-1}  & & & success. The new updates have a 96\% success rate, \\ \cline{1-1}  & & & with most incidents resolved. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Stability and performance across epochs. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metrics** & **Epoch 1** & **Epoch 2** & **Epoch 3** & **Epoch 4** & **Average** \\ \hline \hline CPU usage (\%) & 45.6 & 48.2 & 46.5 & 47.9 & 47.1 \\ Memory usage (\%) & 68.2 & 69.8 & 68.9 & 70.3 & 69.3 \\ Response time (ms) & 28 & 29.1 & 27.8 & 28.7 & 28.5 \\ Transfer rate (Mbps) & 980 & 975 & 990 & 985 & 982.5 \\ Error rate (\%) & 0.3 & 0.4 & 0.2 & 0.3 & 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance metrics in different eras. attacks to SQL injection attempts. GPT-3's ability to generate coherent and contextual text was instrumental in obtaining realistic test data that reflected real-world threats. Table 5 presents a detailed comparison between AI-generated data sets and traditional data sets used in cybersecurity. This table evaluates the effectiveness of AI in creating data sets for different types of cyber threats. The percentages indicate the accuracy and quality of AI-generated data compared to traditional data sets. Across all threat types, AI-generated datasets outperform traditional ones, highlighting the significant role of AI in improving the quality of data used in cybersecurity training environments. These results support the claim that AI has effectively contributed to the generation of test data in this field, strengthening cybersecurity systems' training and evaluation capabilities. On the other hand, DALL-E has proven valuable in analyzing and classifying visual safety data, such as images and graphics. Its ability to understand visual content and generate contextually relevant descriptions has improved accuracy in identifying threat patterns in visual data. This has led to greater effectiveness in detecting attacks that involve visual components, such as malware disguised in image files or suspicious traffic patterns in graphs. Table 6 presents an analysis of the accuracy of DALL-E in classifying images related to cyber threats. Each row in the table corresponds to a specific type of image, and the classification accuracy is provided as a percentage. The results demonstrate that DALL-E is highly accurate in classifying cybersecurity-related images, with accuracy scores greater than 90% across all image types tested.