## 6 Conclusions  
The created model is designed to evaluate the impact of multilevel security on database system functionality while addressing some of the fundamental challenges associated with multilevel database security. The architecture of system security, along with its legal regulations and access control strategies, is both significant and challenging. This has been analyzed through multilevel data, distributed queries, and the importance of security policies. Additionally, several challenges related to developing a secure system prototype are summarized. Accordingly, this model, known as MLS/DBMS, is designed to evaluate authentication samples and assist developers in creating a restriction prototype for a system. Database security operates at the tuple level when using the mandatory access control paradigm. This approach combines user-level and data-level security with database security principles. By enforcing secure access standards, it ensures higher security, providing better data protection. To enhance security, two cryptographic algorithms were integrated: one based on stream ciphers and the other on block ciphers, including RSA and AES. While these algorithms provide a high level of security, they also impact performance in terms of elapsed time and memory usage. \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)** & 91.63013 & 0.007783 & 60674.18 & 5.903371 & 151.9719 & 0.011665 \\ \hline
**Encryption Time (ms)** & 0.127875 & 0.076438 & 108.1653 & 68.02092 & 0.190944 & 0.10716 \\ \hline
**Decryption Time (ms)** & 1.353733 & 0.037877 & 1127.767 & 33.55047 & 3.563766 & 0.071877 \\ \hline
**Memory Usage (bytes)** & 95,860,646 & 96,783,129 & 97,546,899 92,800,451 & 96,696,483 92,789,554 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average performance of cryptographic algorithms with MLS. In conclusion, performance metrics like cumulative size and elapsed time were key to the evaluation, revealing clear differences among authentication types. One type showed the highest resource usage, suitable for resource-rich environments, while another offered the fastest response times, ideal for speed-focused scenarios. The third type balanced resource demands and efficiency, making it adaptable. Additionally, incorporating metrics during the generation of cryptographic algorithms enhances the MLS/DBMS model. AES encryption and decryption times showed a 1.0-5.0% improvement across all types, boosting overall efficiency. The developed model has shown improvement in its limitations and can be further enhanced in future studies, particularly in areas such as user experience and security robustness. Future work could focus on applying metaheuristic algorithms to find the optimal solution for the best constraints while integrating the MLS/DBMS model. Various metaheuristic algorithms, such as Fitness Dependent Optimizer (FDO) [38; 39] and Lagrange Elementary Optimization (LEO) [40], could be incorporated into the model. Additionally, researchers could explore hybridizing these techniques for security enhancement within game applications or statistical algorithms [41]. Furthermore, integrating MLS/DBMS with neural networks could improve accuracy in solving authentication problems [42]. Conceptualization, A.M.A. ; methodology, H.S.A. and A.M.A. ; software, H.S.A. ; validation, H.S.A. and A.M.A. ; formal analysis, A.M.A. ; data curation, A.M.A. ; writing--original draft preparation, H.S.A. and A.M.A. ; writing--review and editing, H.S.A. and A.M.A. ; visualization, A.M.A. ; supervision, A.M.A. ; project administration, H.S.A. and A.M.A. ; funding acquisition, H.S.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The original contributions presented in this study are included in the article.