# Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study
## 4 Results and Discussion
### Issues and Perspectives  
Parameter Tuning. It can be observed from the experiments that parameter tuning is the common problem among all four DL algorithms. The parameters in the DL models consist of the number of layers in the neural networks, number of neurons (units) in each layer, number of kernels and kernel size (for CNN), learning rate, dropout rate, number of epochs, batch size, etc. [17]. There is no standard and specific guideline for setting these parameters so that the highest performance accuracy can be achieved. Manual fine-tuning and conducting a series of experiments through trial and error are standard practices among researchers who work with DL models. However, this process is tedious, time-consuming, and labor-intensive. One possible solution to overcome the problem of manual parameter tuning is to use optimization techniques to fine-tune the parameters and shorten the tuning process [64]. Accuracy Deficiency. Accuracy deficiency is another widespread issue among DL models, as accuracy is one of the most critical metrics that are used to evaluate the performance of the selected DL algorithm. Several factors affect the accuracy of a phishing detection model, including the quality of the dataset, the extracted features, the chosen classifier, the parameter settings, etc. Some of the current studies in the literature focused on solving the problem of accuracy deficiency by applying the existing DL algorithms [6; 7; 43]. In contrast, other researchers combined multiple DL techniques in an ensemble model to enhance the detection accuracy [11; 37; 31]. Ensemble DL (EDL) models are formed by stacking various DL algorithms in parallel and are divided into two categories: homogeneous and heterogeneous. A homogeneous EDL architecture is constructed by combining DL algorithms of the same type (CNN-CNN, LSTM-LSTM, GRU-GRU, etc.). In contrast, a heterogeneous EDL model incorporates DL mechanisms of different kinds (CNN-LSTM, \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{10}{c}{**Performance Metrics**} \\ \cline{3-13}
**Ref.** & \multirow{2}{*}{
\begin{tabular}{c} **DL** \\ **Algorithm** \\ \end{tabular} } & \multicolumn{10}{c}{**Conventional**} & \multicolumn{10}{c}{**Additional**} \\ \cline{3-13}
**Algorithm** & \begin{tabular}{c} **FPR** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **FNR** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **ACC** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **PR** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **RC** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **F1** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **AUC** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Training** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Testing** \\ **Time** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Parameter** \\ **Size** \\ \end{tabular} &
\begin{tabular}{c} **Memory** \\ **Storage** \\ **(MB)** \\ \end{tabular} \\ \hline
[18] & DNN & 1.80 & 3.30 & 97.50 & 97.70 & 96.70 & 97.20 & - & - & - & - & - & - \\
[28] & CNN & - & - & 97.20 & 96.90 & 98.10 & 97.50 & - & 10.67 & 0.472 & 27,985 & - \\
[6] & MLP & - & - & 96.65 & 96.65 & 96.65 & 96.65 & - & - & - & - & - \\  & DNN.BA 1  & - & - & 95.76 & - & - & 95.70 & - & - & - & - \\  & DNN.HA 2  & - & - & 95.00 & - & - & 94.93 & - & - & - & - \\  & DNN.FA 3  & - & - & 96.65 & - & - & 96.61 & - & - & - & - \\  & DNN & 3.01 & 2.47 & 97.29 & 97.53 & 97.53 & 97.53 & 99.40 & 8.38 & 0.470 & 1,507 & 172 \\ Our & CNN & 3.50 & 3.39 & 96.56 & 96.61 & 97.09 & 96.85 & 99.51 & 0.9 & 0.263 & 3,745 & 325 \\ study & LSTM & 1.80 & 3.55 & 97.20 & 96.45 & 98.63 & 97.53 & 99.11 & 169.17 & 0.804 & 66,689 & 184 \\  & GRU & 2.48 & 3.94 & 96.70 & 96.06 & 98.03 & 97.04 & 98.79 & 55.07 & 1.447 & 149,505 & 184 \\ \hline \hline \end{tabular}  
* Parameter settings for DNN using Bat Algorithm. 2 Parameter settings for DNN using Hybrid Bat Algorithm. 3 Parameter settings for DNN using Firefly Algorithm. [ENDFOOTNOTE]  
\end{table}