# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 4 Result and Discussion
### Performance Comparison with the Recent Related Works  
The performance of our proposed model was compared with recent state-of-the-art approaches in the domain of cybersecurity. As shown in Table 11, we evaluated our model on two public datasets Gram-bedding and Kaggle in addition to our custom dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class and Metrics** & **Precision** & **Recall** & **F1-score** & **Support** \\ \hline
0 & 0.95 & 0.95 & 0.95 & 9663 \\
1 & 0.95 & 0.95 & 0.95 & 9537 \\ Accuracy & & & 0.95 & 19,200 \\ Macro average & 0.95 & 0.95 & 0.95 & 19,200 \\ Weighted average & 0.95 & 0.95 & 0.95 & 19,200 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The classification report for the Gram-bedding dataset.  
Figure 16: ROC-AUC curve of the embedding dataset.  
Notably, the Kaggle and Gram-bedding datasets were applied to the Char2B model without any data validation processes. The LogBERT-BiLSTM model demonstrated exceptional performance on the CSIC 2010 dataset, achieving 99.50% accuracy with flawless precision and recall rates of 100%, yielding a perfect F1-score of 100%. These results underscore the efficacy of its integrated architecture. Similarly, the CharBiLSTM-Attention model attained marginally higher performance on a custom dataset, achieving 99.55% accuracy, with 99.64% precision and 99.43% recall, resulting in an F1-score of 99.54%. The model's attention mechanism likely enhanced its feature extraction capabilities.  
In contrast, the BERT model exhibited inconsistent performance across datasets on the GitHub dataset; it achieved 96.71% accuracy, with 96.25% precision and 96.50% recall (F1-score not reported). On the ISX 2016 dataset, it reached 99.98% accuracy, though precision and recall metrics were unavailable.  
Our proposed Char2B model delivered robust results on the custom dataset: 98.50% accuracy, 98.27% precision, 98.69% recall, and 98.48% F1-score. On the Kaggle dataset, it had 99.69% accuracy, 99.84% precision, 99.54% recall, and a 99.69% F1-score, demonstrating strong adaptability across diverse contexts.  
Collectively, these findings indicate that Char2B competes effectively with state-of-the-art benchmarks like LogBERT-BiLSTM and CharBiLSTM-Attention, particularly in scenarios where its hybrid architecture leverages dataset-specific features.  
This study aimed to develop a deep learning model capable of detecting malicious URLs that pose significant cybersecurity threats. The Char2B model developed from the baseline BERT model to evaluate whether a fused architecture could yield superior performance. Using the same dataset, our proposed model outperformed the baseline in terms of detection accuracy and FPR efficiency. For instance, the study in [15] achieved a detection accuracy of 96.71%. Using this as a baseline, our model achieved 98.35% detection accuracy with a 0.018 FPR and a 98.98% ROC-AUC, effectively distinguishing between malicious and legitimate URLs even when tested on unseen data. Compared to the baseline, our model achieved an improvement of 0.15% on the custom dataset and 2.98% on the Kaggle dataset. Overall, these experiments suggest that fusing deep learning models, as done in Char2B, offers enhanced performance compared to relying on a single model architecture.