## 4 Results and Discussion
The classifier's classification capability will be captured this way, allowing us to categorize distinct instances of binary classification (normal and attack instances), multiclass classification, and port scan. The classification report contains information on the accuracy, precision, recall, and F1 score. Regarding detection accuracy, all classifiers achieved a satisfactory level of success, with XGBoost achieving the highest level of success and NB achieving the lowest level of success. Conversely, the XGBoost classifier achieved the lowest false-positive rate (FPR) with a value of 0.06, as shown in Table 3. This demonstrates that it is able to categorize network traffic cases with the lowest possible number of false positives. A detailed performance analysis of the multiclass classification performance is shown in Appendix A. Figure 5 depicts the performance of the classifiers in binary classes, such as normal and attack examples, which provides a better insight into the classifier's performance. The XGBoost classifier outperformed the other classifiers in terms of average recall, F1 score, and precision. It is possible to have a better understanding of how well the classifiers work by looking at the average performance of the binary classifiers in two classes. With respect to average recall accuracy and F1 score, the XGBoost classifier is found to be superior to other classifiers. Figure 6 shows the classification accuracy for multiclass classification. A high percentage of attacks are detected by all classifiers in multiclass classification, where the performance is similar to the binary class classification. The k-NN and NB classifiers have the lowest detection accuracy for TCP, UDP, OS fingerprinting, and keylogging. Both the XGBoost and DT produced similar findings; however, the XGBoost had the best detection accuracy for both attack and nonattack. The precision of the classifiers in multiclass classification is presented in Figure 7. In all attacks and typical cases, the XGBoost, GRB, and DT classifiers attained a precision of between 84 and 99.99. The naive Bayes and k-NN have obtained a precision of 5-72 percent in all types of attack and normal instances, excluding the exception of DDoS TCP attacks, where naive Bayes has reached a precision of 82.1 percent, and keylogging attacks, where k-NN achieved a precision of 98.7. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**No** & **Algorithms** & **Accuracy (\%)** & **Precision** & **Recall** & **F1 Score** & **FPR** \\ \hline
1 & NB & 96 & 55 & 50 & 53 & 0.90 \\
2 & k-NN & 97.1 & 61 & 85 & 75 & 0.64 \\ \hline
3 & DT & 97.9 & 93 & 93 & 93 & 0.16 \\ \hline
4 & GRB & 98.3 & 57 & 88 & 63 & 0.87 \\ \hline
5 & XGBoost & 99.9 & 97 & 99 & 98 & 0.06 \\ \hline \end{tabular}
\end{table} 
Table 3: Average performance metrics of binary classification. Figure 5: Average recall, precision, and F1 measure of the classifiers. Figure 6: Accuracy of multiclass classification. Figure 7: Precision of multiclass classification. A multiclass classification system, except for keylogging attacks and normal cases, achieved recalls of 83-100% in all attacks and normal cases, respectively, in Figure 8, depicting the results of the multiclass classification system. There was a negligible difference in recall value between the two methods, except in the case of keylogging attacks (NB reported an 86% rate of recall). Regarding attacks and regular situations, classifiers such as the XGBoost, GRB, and DT classifiers all earned F1 scores of 82-100%, whereas NB and k-NN only received F1 scores of 4-70% in all but OS fingerprinting assaults, when k-NN scored an impressive 88%, as shown in Figure 9.  
Figure 8: Recall of multiclass classification. Figure 9: F1 score of multiclass classification. The proposed model is compared with the existing work, as shown in Figure 10. In the reinforced learning approach, the random forest method [8] is applied, which has obtained an accuracy of 99.54%.