## 6 Conclusions  
This study proposed an innovative phishing-detection model using the Keras Embedding Layer with GloVe to take advantage of the semantic and syntactic features of the web page text. Our proposed model used automatic features of the text contained in web pages,  
Figure 14: Accuracy comparison of the four algorithms.  
Figure 15: Loss comparison of the four algorithms.  
applying word-level embedding methods to represent these features in vector form. These vectors were then input to the LSTM, BiLSTM, GRU, and BiGRU algorithms to identify phishing pages.  
According to the literature review conducted in this study, there still is a research gap in web page text analysis using NLP and DL. Although several articles on combating phishing with NLP and DL were initially found, most were oriented toward combating phishing emails. As for tackling web pages, most were oriented toward working on URLs and not on the text of the web pages.  
After the experiment, it can be determined that BiGRU was the DL algorithm that performed better with the data previously analyzed with NLP than the other three DL algorithms, LSTM, BiLSTM, and GRU. On the other hand, BiLSTM gave the worst results, even below those expected. The validity of our model was demonstrated using the K-fold cross-validation technique, which yielded a mean accuracy of LSTM 96.71%, BiLSTM 97.20%, GRU 97.29%, and the best, BiGRU 97.39%.  
The model was tested with three text lengths obtained from web pages, L = 200, L = 500, and L = 1000. However, the results did not vary much between them, so it was determined that 200 was the optimal size to perform this analysis. Internally, we ran experiments with words below 200, but this resulted in very poor results, which is why text lengths below 200 were not taken into consideration for the experiments.  
In future work, we plan to evaluate our model with other word embeddings mechanisms, such as Fast Text and Word2 Vec, to evaluate their performance in the NLP domain. In addition, to improve the performance of DL algorithms, we plan to implement an attention-based DL architecture, which can differentiate which parts are more critical than others, depending on the context.  
While we obtained acceptable results to face phishing attacks with NLP execution and DL algorithms, we plan to conduct a comparative study in which we can tune the parameters of both the embedding layer and DL algorithm layers for the best results. Also, when performing the SLR, we noticed that it is necessary to perform an SLR about mitigating phishing attacks only with NLP and DL. As well as it is also necessary to perform an algorithm that detects these attacks using ensemble methods of at least two DL Algorithms on various levels, such as URL, third-party information, and Web content level.  
Conceptualization, E.B.-A. and S.S.-G.; Methodology, E.B.-A., W.F. and S.S.-G.; Software, E.B.-A. and G.R.-G.; Validation, E.B.-A. and S.S.-G.; Formal analysis, W.F. and S.S.-G.; Investigation, E.B.-A.; Resources, D.N.-A. and G.R.-G.; Data curation, E.B.-A., W.F. and D.N.-A.; Writing--original draft, E.B.-A., S.S.-G. and G.R.-G.; Writing--review & editing, E.B.-A. and W.F.; Supervision, W.F. and S.S.-G.; Funding acquisition, E.B.-A., W.F. and D.N.-A. All authors have read and agreed to the published version of the manuscript.  
The authors' funds financed this research.  
Not applicable.  
The authors declare no conflict of interest.