# Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study
## 5 Conclusions and Future Works
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Reference** & **Dataset** & \begin{tabular}{c} **Dataset** \\ **Size** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Layers** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Units** \\ \end{tabular} & \begin{tabular}{c} **Learning** \\ **Rate** \\ \end{tabular} & \begin{tabular}{c} **Dropout** \\ **Rate** \\ \end{tabular} & \
\begin{tabular}{c} **Batch** \\ **Size** \\ \end{tabular} &
\begin{tabular}{c} **Epoch** \\ **Epoch** \\ \end{tabular} \\ \hline
[41] & \
\begin{tabular}{c} PhishTank \\ Common \\ Crawl \\ \end{tabular} & 1.5 million & 1 & 128 & 0.0001 & - & - & 25 \\  &
\begin{tabular}{c} PhishTank \\ \end{tabular} & 153,788 & & & & & & & \\
[46] &
\begin{tabular}{c} Openphish \\ Alexa \\ \end{tabular} & 7212 & 1 & 100 & 0.001 & 0.2 & 64 & 30 \\
[42] &
\begin{tabular}{c} PhishTank \\ Alexa \\ \end{tabular} & 170,552 & & & & & & & & \\
[42] &
\begin{tabular}{c} PhishTank \\ Alexa, DMOZ, \\ \end{tabular} & 2119 & - & 4 & 0.001 & - & - & 700 \\
[31] &
\begin{tabular}{c} Alexa, DMOZ, \\ \end{tabular} & 611,894 & 1 & 70 & 0.001 & - & - & 500 \\
[44] &
\begin{tabular}{c} Vaderetro \\ Alexa \\ \end{tabular} & 2000 & 1 & - & - & - & - & - & 200 \\
[43] &
\begin{tabular}{c} PhishTank \\ Yahoo \\ \end{tabular} & 2000 & 5 & 128 & 0.001 & - & 128 & - \\
[33] & \
\begin{tabular}{c} PhishTank \\ DMOZ \\ \end{tabular} & 1,021,758 & - & - & - & - & 64 &
\begin{tabular}{c} 20/140/ \\ 256/578 \\ \end{tabular} \\
[35] &
\begin{tabular}{c} Virus Total \\ \end{tabular} & 1 & 32 & 0.0001 & - & 32 & 200 \\
[10] & \
\begin{tabular}{c} PhishTank \\ Alexa/ Amazon \\ \end{tabular} & 21,303 & 1 & 128 & 0.001 & 0.5 & 64 & 10 \\  &
\begin{tabular}{c} PhishTank \\ \end{tabular} & 4,820,940 & 2 & 128 & 0.0001 & 0.5 & - & - \\
[11] &
\begin{tabular}{c} Openphish \\ Alexa \\ \end{tabular} & 4,820,940 & 2 & 128 & 0.0001 & 0.5 & - & - \\
[47] & \begin{tabular}{c} UCI \\ \end{tabular} & 2456 & 3 & - & 0.0001 & - & - & 800 \\  & \
\begin{tabular}{c} 4 \\ 5 \\ \end{tabular} &
\begin{tabular}{c} 0.01 \\ \end{tabular} & & & & & & & 900 \\
[45] &
\begin{tabular}{c} PhishTank \\ \end{tabular} & 450,176 & 1 & 10 & - & 0.2 & - & 10 \\
[36] &
\begin{tabular}{c} OpenPhish \\ Alexa \\ \end{tabular} & 52,000 & - & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Previous research works on LSTM. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Exp. **} & \multirow{2}{*}{ \begin{tabular}{c} **Hidden** \\ **Layer** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Neurons in** \\ **Each Hidden** \\ **Layer** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **FPR** \\ **(\%)** \\ \end{tabular}  } & \multirow{2}{*}{
\begin{tab \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Experiment** & **Dropout** & **Rate** & **FPR (\%)** & **FNR** & **Precision** & **Recall** & **F1-Score** & **AUC** & **Accuracy** & **Time** \\
**(Exp.)** & **Rate** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline C5-1 & 0.1 & 5.31 & 2.98 & 97.02 & 95.40 & 96.21 & 99.36 & 95.93 & 54 \\ C5-2 & 0.2 & 5.37 & 3.32 & 96.68 & 95.57 & 96.12 & 99.45 & 95.75 & 53 \\ C5-3 & 0.3 & 3.53 & 5.36 & 94.64 & 97.20 & 95.90 & 99.21 & 95.43 & 81 \\ C5-4 & 0.4 & 3.96 & 4.23 & 95.77 & 96.93 & 96.34 & 99.27 & 95.88 & 54 \\ C5-5 & **0.5** & 3.50 & 3.39 & 96.61 & 97.09 & 96.85 & 99.51 & **96.56** & 54 \\ \hline \end{tabular}
\end{table} 
Table 10: Performance metrics of CNN at different dropout rates. (Number of kernels = 16, kernel size = 3, learning rate = 0.005, batch size = 32, epoch = 50).