## 5 Results and Discussion
This suggests adaptability for both moderate and highly secure environments, producing an unexpectedly robust performance across varied security requirements. Type 2 also displays versatility, with slightly higher scores in the C level (7), but has the lowest frequency at Top Secret (5), which may imply moderate suitability for highly sensitive data. Type 3, in contrast, places a strong emphasis on security, achieving the highest score at the S level (8). However, it maintains relatively consistent performance across the other levels, except for the T level (5), which is an unexpected result. This outcome seems unusual, especially considering previous research on case sensitivity and improvements, where all levels, including T level, should have demonstrated stronger performance. This pattern suggests Type 3's potential for environments prioritizing data sensitivity and robust security. Overall, each authentication type shows distinct patterns in security level suitability, guiding their use based on confidentiality and resource requirements. For further experimental evaluations, the model was analyzed as described in the methodology section. The model was integrated with cryptographic algorithms to assess four key features: key generation elapsed time, encryption elapsed time, decryption elapsed time, and memory usage during the process. For this evaluation, these features were calculated using the RSA and AES algorithms in the application without employing the MLS model. Additionally, for comparison and accuracy, the results were obtained using Python programming, as detailed in the previous section. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Statistical**} & \multicolumn{2}{c}{**Type1**} & \multicolumn{2}{c}{**Type2**} & \multicolumn{2}{c}{**Type3**} \\ \cline{2-7}  & **Size** & **Time** & **Size** & **Time** & **Size** & **Time** \\ \hline Sum & 408 & 3.1874548 & 464 & 14.6744821 & 432 & 15.4596281 \\ \hline Average & 40.8 & 0.31874548 & 46.4 & 1.46744821 & 43.2 & 1.54596281 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A statistical experiment evaluating three specific authentication types. Table 4 presents the results obtained without utilizing the MLS model. The results for both selected algorithms indicate the average authentication times for username and password authentication and are detailed in Appendix B. It is observed that RSA generally requires more elapsed time compared to AES. This is primarily because RSA involves more complex mathematical computations, making its key generation process more time-consuming. However, RSA is also more scalable and secure as an algorithm. Furthermore, Type 2 authentication requires more time than the other two types due to the added complexity in managing the relationship between the access username and password. While there are slight differences in average memory usage across the algorithms, these differences are minimal and do not show significant gaps. Table 5 presents the evaluation results using our generated model (MLS/DBMS). The results show that the AES algorithm requires less time for key generation compared to RSA, though significantly. Type 2 authentication involves more calculations but offers higher \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Features**} & \multicolumn{2}{c}{**Type1**} & \multicolumn{2}{c}{**Type2**} & \multicolumn{2}{c}{**Type2**} \\ \cline{2-7}  & **RSA** & **AES** & **RSA** & **AES** & **RSA** & **AES** \\ \hline
**Key Gen Time (ms)** & 98.52702 & 0.00828 & 60762.8900 & 6.0300 & 153.4295 & 0.0117 \\ \hline
**Encryption Time (ms)** & 0.13183 & 0.07721 & 109.5900 & 69.4800 & 0.2448 & 0.1128 \\ \hline
**Decryption Time (ms)** & 1.34033 & 0.03865 & 1143.7800 & 35.3200 & 3.2106 & 0.0741 \\ \hline
**Memory Usage (bytes)** & 97,816,986 & 97,859,584 & 97,644,544 & 97,684,685 & 97,673,215 & 97,673,215 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average performance of cryptographic algorithms without using MLS.