## 4 Results and Discussion
### Analysis of Experimental Results  
Unlike conventional techniques, our method generates a new dataset by combining the created unknown attacks dataset with the original dataset. This new dataset is solely used as a testing set to evaluate the LVAE's ability to detect unknown threats. This novel testing method provides valuable insights into the performance of our approach. During the training phase, samples are randomly selected from the original dataset to train the model. Tables 3-5 display the individual classification results obtained using each method. These results represent the best classification outcome achieved by comparing the individual results obtained using each method. The data presented in the tables indicate the best classification result obtained for each sample size. Table 3 illustrates the individual results obtained from various methods for detecting unknown attacks, with sample sizes ranging from 10,000 to 30,000. When the sample size is 10,000, a comparison of the individual results reveals that Random Forest achieves the highest training accuracy of 99.95%, while Gated Recurrent Unit achieves the highest testing accuracy of 99.01%. Multilayer Perceptron and Decision Tree achieve the best precision and recall, respectively. However, the Gated Recurrent Unit demonstrates the best overall model performance, as it achieves the highest F1 score. As the sample size increases, Random Forest maintains the highest training accuracy, while the Gated Recurrent Unit continues to achieve the best testing accuracy. Multilayer Perceptron retains the highest precision, and Random Forest and Gated Recurrent Unit exhibit the best recall and F1 score, respectively. These results showcase the specific performance of each method for detecting unknown attacks with varying sample sizes. All methods perform well, further highlighting the superiority of the LVAE method in detecting unknown types of attacks. Table 4 presents a detailed analysis of the individual results obtained using various methods, with a sample size ranging from 40,000 to 60,000. At a sample size of 40,000, we observe that the Naive Bayes method achieves a lower training accuracy, while the other methods perform well. Random Forest performs the best, achieving the highest training accuracy, recall, and \(F1\). Multilayer Perceptron, Gradient Boosting, and Gated Recurrent Unit achieve the highest precision, recall, and test accuracy, respectively. As the sample size increases, Random Forest, Gated Recurrent Unit, and Gradient Boosting exhibit the highest performance. As the number of samples increases, we see steady progress in all metrics, indicating that our model successfully learns the underlying data properties. This demonstrates the effectiveness of our suggested LVAE approach, particularly in creating unknown attacks. Table 5 displays the results obtained from various methods, with a sample size ranging from 70,000 to 90,000. At a sample size of 70,000, we find that Random Forest performs the best, achieving the best performance in almost every metric. Gradient Boosting and Naive Bayes also achieve the highest F1 scores, while Support Vector Machines and Gated  
Recurrent Units achieve the highest precision and test accuracy. As the number of samples increases, Random Forest, Gradient Boosting, and Gated Recurrent Unit achieve the best F1 scores, and the experimental results for all methods improve. In summary, our suggested LVAE approach improves the reconstruction loss component by utilizing the log-cosh loss function, leading to better performance in identifying unknown attacks. Through extensive experimentation, we demonstrated that the LVAE consistently and reliably detects unknown attacks. Despite the high performance achieved using our method, there are still some limitations. For example, the Gaussian-based Naive Bayesian approach performs poorly in detecting unknown attacks with a sample size of 30,000. This is partially attributed to the large error in the testing data, which negatively affects the predictive power of the model.