# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 4 Result and Discussion
### Performance Evaluation on Other Datasets
#### 4.6.2 Gram-Bedding Dataset  
We assessed our model using a Gram-bedding dataset with the same parameter, which offered a thorough summary of how well the model performs in identifying events as harmful or benign [25]. As shown in Figure 15, from the top-left corner of the matrix, 9172 cases accurately classified as benign, whereas 491 benign instances mistakenly classed as malicious. On the other hand, 499 harmful occurrences mistakenly classified as benign in the bottom-left quadrant, whereas 9038 malicious instances accurately recognized in the bottom-right corner. With a high true positive rate of 0.9477 and comparatively few false positives, at a rate of 0.0508, and false negatives, at a rate of 0.0522, the model does well overall, especially when it comes to detecting harmful instances. This suggests that there is a high potential for precise classification between the two groups.  
The training and testing performance evaluation of our proposed model on the Gram-bedding dataset shown in Table 9. Hence, 94.25 and 94.84 accuracy was registered for the training and testing sets, respectively.  
Figure 16 presents a plot of the TPR against the FPR and gives information about how well the model can differentiate between benign and malicious cases. This curve is  
Figure 15: Confusion matrix of the Gram-bedding dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class and Metrics** & **Precision** & **Recall** & **F1-score** & **Support** \\ \hline
0 & 1.00 & 1.00 & 1.00 & 9456 \\
1 & 1.00 & 1.00 & 1.00 & 9519 \\ Accuracy & & & 1.00 & 18.975 \\ Macro average & 1.00 & 1.00 & 1.00 & 18,975 \\ Weighted average & 1.00 & 1.00 & 1.00 & 18,975 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The classification report for the Kaggle dataset.  
noteworthy for being close to the upper-left corner of the plot, which signals outstanding model performance due to its high true positive rate and low false positive rate. With an area under the ROC curve (AUC) of 0.9890, the model has a 98.90% chance of accurately differentiating between the two groups. This high AUC value demonstrates the model's great discriminatory capacity in recognizing harmful instances while limiting false alarms, hence reinforcing its usefulness. The diagonal line in the ROC curve indicates that the random line is used as the boundary between two classes. If the TRP and FPR curve lay in this line, the class is not classified properly or the dataset contains a single class.  
We summarize the performance of the proposed model on the Gram-bedding dataset using the classification report as shown in Table 10. The classification report shows that the model performs **consistently well** across both classes (zero and one) as precision, recall, and F1-score are all 0.95 for both classes, indicating balanced performance.  
Moreover, accuracy is also 95%, meaning the model correctly predicted 95% of all 19,200 samples. The macro and weighted averages match individual class metrics, confirming no significant class imbalance or bias. Therefore, the model demonstrates strong, uniform performance in classifying both categories, suggesting it is reliable and well generalized for the task. Further improvements may focus on boosting accuracy beyond 95% or testing robustness on unseen datasets.