## 4 Results and Discussion
### Results
#### 4.1.2 Voting Classifier  
Based on the first dataset, CIC-MalMem-2022, the model was trained and had an accuracy result of 99.96%. According to the confusion matrix in Figure 12, the model correctly predicted 5806 malicious and 5803 benign instances. However, one malicious instance was incorrectly predicted as benign, and three benign instances were incorrectly predicted as malicious. The accuracy result for the CIC-IDS-2018 dataset is 99.58%, and based on Figure 13, the model correctly predicted 2077 malicious instances and 4431 benign instances. In addition, 27 benign instances were incorrectly predicted as malicious, while no malicious instances were incorrectly predicted as benign. The accuracy result for the CIC-IDS-2017 dataset is 99.31% for this model, and the confusion matrix in Figure 14 indicates that the model correctly predicted 6279 malicious and 6982 benign instances. This model made incorrect predictions regarding 69 malicious instances and 22 benign instances. The learning curve for the CIC-MalMem-2022 dataset in Figure 15 indicates that the training score is 100% accurate in each cycle, meaning that the model is well trained to handle the data. In contrast, cross-validation scores are accurate between 99.80% and 99.90%, demonstrating the model's ability to identify and learn new datasets. Compared with the cross-validation score, which ranges from 94% to 96%, the CIC-IDS-2018 dataset's training scores achieved 99.99% accuracy in every cycle, as shown in Figure 16. With more examples of data, a classifier learns more about the data, and as the number of training examples increases, the cross-validation score also increases at a slower rate. For the CIC-IDS-2017 dataset, as shown in Figure 17, the training score starts at 99.51% and then drops to 99.46%, while the cross-validation score starts at 99.30% and then decreases to about 99.28% in accuracy. As a result of its effectiveness in learning, the Voting classifier will likely generalize well to new data. There are two options for the voting hyperparameter in the Voting model: hard or soft. The hard option is based on a majority vote, while the soft option is based on the average probability. In the CIC-MalMem-2022 dataset, the soft parameters were found to be the most effective, with a mean test score of 99.90% and a mean score time of 0.38 s. On the other hand, the hard option results in a mean test score of 99.88%, and the mean test time is 0.76 s. Moreover, the best parameter for the CIC-IDS-2018 dataset is also the soft value, with a mean test score of 99.44% and a mean score time of 0.22 s. Furthermore, the best parameter value for the CIC-IDS-2017 dataset is also soft, with a 99.39% mean test score and a 0.78 s mean test time.  
Figure 11: Hyperparameter tuning for AdaBoost on CIC-IDS-2017 dataset.  
Figure 14: Confusion matrix for Voting classifier on CIC-IDS-2017 dataset.  
Figure 12: Confusion matrix for Voting classifier on CIC-MalMem-2022 dataset.  
Figure 13: Confusion matrix for Voting classifier on CIC-IDS-2018 dataset.  
Figure 16: Learning curve for Voting classifier on CIC-IDS-2018 dataset.  
Figure 17: Learning curve for Voting classifier on CIC-IDS-2017 dataset.  
Figure 15: Learning curve for Voting classifier on CIC-MalMem-2022 dataset.