# Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study
## 4 Results and Discussion
### Results with GRU  
Last but not least, parameter settings for the GRU algorithm were set to (30 128 128 1) (Figure 9), learning rate = 0.001, dropout rate = 0.5, batch size = 32, and epoch = 200. The highest accuracy that the model could achieve with this set of parameters was 96.70%. Detailed experiments of how to achieve this optimal set of parameters are provided in Appendix A (Tables A21-A25). Similar to LSTM, GRU also required a long duration of training time. As a result, a more complex network architecture with more layers or neurons only increased the computational cost and reduced the model efficiency. The loss and accuracy versus the number of epochs for different DL algorithms during training and validation are shown in Figure 10; as the number of epochs increased, the performance accuracy increased while the loss function decreased. The performance metrics of four DL models are displayed in Table 8 and Figure 11. It is observed from the experiments that the accuracy of the DNN model was slightly higher than that of the other three DL algorithms. In addition, the amount of time required to train and test the DNN model was relatively low. DNN also had the smallest parameter size and occupied the least memory storage. In contrast, CNN had the lowest measure for accuracy compared with the other three DL mechanisms, yet it required the shortest duration for model training and testing. The parameter size for CNN was more significant than that for DNN, but CNN consumed the most computational power in terms of GPU storage. Meanwhile, to achieve an almost equivalent accuracy level as DNN, many iterations were involved in the training phase of LSTM and GRU models, which made their training time longer than the others. As the number of neurons in LSTM and GRU models was higher, their parameters were also more significant. However, the GPU capacity requirement for LSTM and GRU was less than that for CNN. To sum up, no DL algorithm provided the best measure across all performance metrics. Each DL technique has its pros and cons;  
Figure 8: Optimal LSTM architecture. Figure 9: Optimal GRU architecture. In Table 9, results obtained from the empirical analysis of this study are compared with those attained from other authors using the same dataset. In previous studies, the authors used only one type of DL algorithm, such as DNN, CNN, or MLP (multi-layer perceptron). However, four different DL architectures from various categories (supervised, unsupervised, and hybrid) were implemented in our research work. Moreover, dropout rate and batch size, which were not specified in some studies [6,18,28], were included in our empirical analysis. In addition, it is also observed from the table that although different authors used the same algorithm, their optimal set of parameters and accuracy results were not the same. This implies that researchers still had to perform manual parameter tuning to obtain the optimal parameter settings for their DL models. The authors in [64] suggested that this process could be optimized using swarm intelligence (Bat, Hybrid Bat, and Firefly Algorithm). Yet, their accuracy measures were either equivalent or lower than other authors. Meanwhile, the accuracies obtained from our study are almost as high as other authors. Moreover, we also measured additional metrics to obtain a more comprehensive analysis of different DL algorithms' performance in detecting phishing websites. Table 10 provides a comparison of various performance metrics between our research work and previous studies using the same dataset. Compared with [64], our study achieved higher accuracy and F1-Score for DNN and included various metrics not measured in [64]. Since MLP is a subset of DNN, MLP results from [6] could be compared with DNN from our empirical analysis. The obtained results showed that our DNN model outperformed  
Figure 11: Performance metrics of four DL algorithms.