# Detecting Phishing URLs Based on a Deep Learning Approach to Prevent Cyber-Attacks
## 5 Performance Evaluation and Comparison with Existing Approaches
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Author, Year** & **Methodology** & **Datasets** & **Limitations** & **Accuracy** \\ \hline \multirow{4}{*}{[48]} & \multirow{4}{*}{[PERSON]} & Hybrid deep learning & & & \\  & & technique that detects the & & The proposed & \\  & & public image frame and & & & technique is focused & \\  & & textual information for URL & & datasets & more on URL image & \\  & & detection utilizing both the & & detection. & \\  & & CNN and LSTM methods. & & & \\ \hline \multirow{4}{*}{[45]} & \multirow{4}{*}{[PERSON] et al., 2023} & A dense forward-backward & & & \\  & & long short-term memory & & & \\  & & (LSTM) model (d-FBLSTM) & \multirow{4}{*}{MUPD} & The proposed model & \\  & & was proposed for the & & & \\  & & detection of phishing URLs. & & & \\ \hline \multirow{4}{*}{[5]} & \multirow{4}{*}{[PERSON] et al., 2022} & URL-based phishing & \multirow{4}{*}{PhishTank and} & The results were not & \\  & & detection based on LTSM & & & compared with a deep & \\  & & and CNN models. & & & learning or machine \\  & & & & learning model. & \\ \hline \multirow{4}{*}{[46]} & \multirow{4}{*}{[PERSON] et al., 2023} & Phishing URL detection & & Not all the evaluation & \\  & & using a quadruplet deep & & measures mentioned & \\  & & neural network based on & & were applied to & \\  & & combining several n-gram & & evaluate the & \\  & & embeddings and word & & performance of the & \\  & & embeddings. & & model. & \\ \hline \multirow{4}{*}{[47]} & \multirow{4}{*}{[PERSON]} & A Phish-armor phishing & \multirow{4}{*}{PhishTank,} & A complex and & \\  & & detection model using deep & & time-consuming & \\  & & recurrent neural networks & & Common Crawl, & computation in \\  & & & content for the detection of & & Raspberry Pi. & \\  & & false URLs. & & & \\ \hline \multirow{4}{*}{[49]} & \multirow{4}{*}{[PERSON] et al., 2023} & An SI-BBA technique with a & & & \\  & & deep learning model for & & & \\  & & identifying phishing & & EDU & \\  & & websites and successfully & & can be improved. & \\  & & classifying them. & & & \\ \hline \multirow{4}{*}{[50]} & \multirow{4}{*}{[PERSON] et al., 2023} & A CNN is used for detection & & & \\  & & with random forest models & & & \\  & & to determine the feature & & & \\  & & significance at & & \\  & & various levels. & & \\ \hline \multirow{4}{*}{[51]} & \multirow{4}{*}{[PERSON] et al., 2023} & This model combines the & & & \\  & multi-head self-attention & & & \\  & mechanism with a CNN and & & UCI phishing & The results can be & \\  & & generative adversarial & & \\  & network model to create a & & & improved further. & \\  & & URL detector. & & & \\ \hline \multirow{4}{*}{[52]} & \multirow{4}{*}{[PERSON] et al., 2020} & A data-driven framework & Based on ten & The framework & \\  & & for detecting phishing & thousand web & focused on phishing & \\  & & webpages. & pages & web pages. & \\ \hline \multirow{4}{*}{[53]} & \multirow{4}{*}{[PERSON] et al., 2020} & Proposes a deep neural & Phishing URLs & The results can be & \\  & & network architecture. & and benign URLs & improved further. & \\ \hline \multirow{4}{*}{Proposed model} & \multirow{4}{*}{-} & A model is proposed for the & & & \\  & & detection of phishing URLs & PhishTank, UNB, & It takes a long time to & \\  & & based on 1D CNN & and Alexa & train the model. & \\ \cline{1-1}  & & architecture. & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the proposed model with existing approaches. In [49] proposed a Phish-armor phishing detection model using deep recurrent neural networks that matched SSL and website content for the detection of false URLs, but the complex model involved time-consuming computation in Raspberry Pi and attained an accuracy of only 90.50% [45]. In [49] proposed an SI-BBA technique with a deep learning model for identifying phishing websites and successfully classifying them using the dataset Phishing URL EDU; they attained an accuracy of 94.8%, but the model showed low precision [46].