## 4 Results and Discussion
### Calculated Cost Analysis of the Various Components of the LVAE Methodology  
We used a personal laptop with a GeForce RTX 3060 laptop GPU to evaluate the LVAE approach. The time requirements for each step in the LVAE approach for different training data quantities are shown in Figures 3-6. Interestingly, the VAE phase consistently required the least amount of time to generate unknown attacks, demonstrating the efficiency of our method. The combination of the VAE and MLP produced the highest time consumption, with the combination of GRU coming in second. The third-highest time consumption was attributed to the combination with SVM, while the other components required comparable amounts of time. When the number of training samples increases, the total amount of time consumed remains relatively low, as observed by examining the time consumption for each LVAE component. This discovery demonstrates the effectiveness of the LVAE in identifying unknown attacks at little computing cost. Figures 3 and 4 show the time consumption at different stages in the LVAE process for sample sizes between 10,000 and 40,000. The VAE component requires the least amount of time to generate unknown attacks, indicating its effectiveness in producing them. While the other components require comparable amounts of time, MLP and GRU demand a substantial amount of time during the classification step. Figures 5 and 6 display the time spent at each stage of the LVAE approach for sample sizes ranging from 50,000 to 90,000, which further supports the earlier findings. The enhanced VAE efficiently produces unknown attacks, which are categorized by a classifier that incorporates eight techniques. The time analysis shown in Figures 3-6 confirms that our approach effectively generates unknown attack data and learns data features quickly. \begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline
[16] & 97.28\% & — & 97\% & 72.81\% \\
[44] & 92.10\% & 59.13\% & 49.12\% & 97.15\% \\
[49] & 96.90\% & 97.70\% & 97.60\% & 97.60\% \\
[39] & 89.78\% & 91.16\% & 95.34\% & 93.13\% \\
[37] & 99.07\% & 98.91\% & 98.95\% & 98.93\% \\
[29] & 97.07\% & 97.05\% & 97.10\% & 97.07\% \\
[25] & 91.33\% & 99.77\% & 89.00\% & 94.10\% \\
[17] & 97.20\% & 81.40\% & 83.60\% & 82.50\% \\
[1] & 98.99\% & 96.99\% & 89.78\% & 93.25\% \\ \hline
**Proposed** & **99.88\%** & **99.78\%** & **99.89\%** & **99.84\%** \\ \hline \end{tabular}  
* A represents the integration of classifiers, i.e., the eight methods of MLP, GaussianNB, DT, RF, SVM, LR, GB, and GRU.  
\end{table} 
Table 6: Comparative study. Tables 3-5 demonstrate the accuracy of the LVAE in detecting unknown threats. Our approach consistently outperforms alternative methods and achieves outstanding accuracy rates when combined with different categorization algorithms, demonstrating its efficacy in identifying unknown attacks. To sum up, the performance of identifying unknown attacks is greatly enhanced by our LVAE approach. The effectiveness of our technique is enhanced by the correct classification of these attacks and the efficient production of unknown attacks. Our LVAE method's efficiency and dependability are confirmed by the testing results and the time consumption analysis. Figure 4: Time consumed in sections between 30,000 and 40,000. Figure 3: Time consumed in sections between 10,000 and 20,000. Additionally, Figure 7 illustrates the evaluation of the loss in our LVAE approach during the generation of unknown attacks. It is evident that the loss rapidly decreases and converges to its minimum value before the 20 th epoch. Subsequently, it maintains a stable state of convergence. These findings, coupled with the results presented in Tables 3-5, provide further evidence of the effectiveness of our LVAE approach. Within just a few training epochs, our approach achieves a high detection rate and quickly converges to the true state of convergence.