# Semi-Supervised Learning for Intrusion Detection in Large Computer Networks
## 4 Experimental Results and Discussion
### Results and Analysis
#### 4.7.17 Comparative Results  
As shown above, the proposed models perform well on limited data due to the very large size of the dataset. Since the dataset is so large that 1% is still a substantial amount, this raises the possibility of simply ignoring the unlabeled data and using the base models on the labeled data. The comparative results for decision tree models with split 1, split  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Base Model** & **Proposed Model** \\ \hline Precision (Macro Avg) & 0.16 & 0.23478 \\ Recall (Macro Avg) & 0.18 & 0.19994 \\ F1-Score (Macro Avg) & 0.13 & 0.16741 \\ Precision (Weighted Avg) & 0.41 & 0.46723 \\ Recall (Weighted Avg) & 0.54 & 0.55308 \\ F1-Score (Weighted Avg) & 0.40 & 0.44911 \\ Accuracy & 0.54 & 0.55308 \\ \hline \hline \end{tabular}
\end{table}
Table 20: Comparative results for logistic regression models: Split 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Base Model** & **Proposed Model** \\ \hline Precision (Macro Avg) & 0.20 & 0.19405 \\ Recall (Macro Avg) & 0.20 & 0.19831 \\ F1-Score (Macro Avg) & 0.17 & 0.16435 \\ Precision (Weighted Avg) & 0.42 & 0.43987 \\ Recall (Weighted Avg) & 0.54 & 0.55135 \\ F1-Score (Weighted Avg) & 0.43 & 0.44469 \\ Accuracy & 0.54 & 0.55135 \\ \hline \hline \end{tabular}
\end{table}
Table 21: Comparative results for logistic regression models: Split 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Base Model** & **Proposed Model** \\ \hline Precision (Macro Avg) & 0.49496 & 0.61501 \\ Recall (Macro Avg) & 0.44648 & 0.48520 \\ F1-Score (Macro Avg) & 0.45134 & 0.51273 \\ Precision (Weighted Avg) & 0.79908 & 0.82203 \\ Recall (Weighted Avg) & 0.81743 & 0.83795 \\ F1-Score (Weighted Avg) & 0.80544 & 0.81859 \\ Accuracy & 0.81743 & 0.83795 \\ \hline \hline \end{tabular}
\end{table}
Table 22: Comparative results for random forest models: Split 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Base Model** & **Proposed Model** \\ \hline Precision (Macro Avg) & 0.58713 & 0.77094 \\ Recall (Macro Avg) & 0.60104 & 0.57212 \\ F1-Score (Macro Avg) & 0.59308 & 0.59138 \\ Precision (Weighted Avg) & 0.84939 & 0.86714 \\ Recall (Weighted Avg) & 0.84928 & 0.86401 \\ F1-Score (Weighted Avg) & 0.84932 & 0.85975 \\ Accuracy & 0.84928 & 0.86401 \\ \hline \hline \end{tabular}
\end{table}
Table 18: Comparative results for decision tree models: Split 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Base Model** & **Proposed Model** \\ \hline Precision (Macro Avg) & 0.20 & 0.19405 \\ Recall (Macro Avg) & 0.20 & 0.19831 \\ F1-Score (Macro Avg) & 0.17 & 0.16435 \\ Precision (Weighted Avg) & 0.42 & 0.43987 \\ Recall (Weighted Avg) & 0.54 & 0.55135 \\ F1-Score (Weighted Avg) & 0.43 & 0.44469 \\ Accuracy & 0.54 & 0.55135 \\ \hline \hline \end{tabular}
\end{table}
Table 21: Comparative results for logistic regression models: Split 3.  
The comparative analysis reveals that the proposed models generally outperform the base models across various metrics, particularly in terms of accuracy and weighted average F1-scores. The inclusion of unlabeled data in a semi-supervised learning framework adds significant value, especially when the amount of labeled data is limited, as in this study. This validates the approach of leveraging both labeled and unlabeled data to enhance the model's ability to generalize and perform effectively in real-world intrusion detection scenarios.