# Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study
## 1 Introduction  
In the past few years, deep learning (DL) techniques have proven to be an effective solution among applications across multiple disciplines, including Internet of Things (IoT), intrusion detection system (IDS), ransomware detection, etc. [1, 2, 3, 4, 5]. Numerous researchers in cyber security have shifted their attention towards DL algorithms. Notably, researchers and security experts have also recognized its significance in the phishing detection domain [6, 7, 8]. During the last few years, website phishing has become one of the most common phishing attacks in cyberspace. Therefore, various anti-phishing solutions have been developed to detect phishing threats early to minimize the security risks and protectthe end-users. Among them, website phishing detection based on DL algorithms has caught much attention in recent studies. Security strategies based on DL mechanisms have become increasingly popular to deal with evolving phishing attacks [9; 10; 11]. There are numerous types of DL techniques designed to solve a specific problem or meet a system's particular requirement; each has its advantages and disadvantages [2; 12; 13].  
Hence, choosing the right approach best fitted to a target application is not an easy task. Especially when phishers keep changing their attacking tactics to leverage the systems' vulnerabilities and the users' unawareness, selecting an inappropriate algorithm would lead to unpredicted outcomes, resulting in a waste of effort and eventually affecting the model's accuracy and efficiency [14]. Therefore, choosing an effective phishing detection model, high in performance accuracy and low in computational power, is a challenging task. The fine-tuning process of DL architectures is another issue that needs to be considered. Motivated to solve this problem, this paper adopted an empirical approach to explore the performance of several DL algorithms, such as deep neural network (DNN), convolutional neural network (CNN), Long Short-Term Memory (LSTM), and gated recurrent unit (GRU). This paper also identified the parameter settings for each DL model and investigated the effects of changing these parameters on the model's performance accuracy. The final goal of this paper was to choose the best DL algorithm with the neural network architecture that produced the maximum accuracy with the minimum computational consumption. The findings from the empirical analysis of this paper also highlight the overlooked issues and future perspectives that encourage researchers to solve these problems.  
This paper continues our previous research work that described a systematic literature review on phishing detection and machine learning [15]. One of the findings from this work suggested that DL algorithms appeared to be an effective solution for detecting phishing attacks, yet they have not been fully exploited. In this regard, an empirical analysis was conducted in this study to explore the most recent DL techniques used for phishing detection. In this paper, the following contributions were made via the empirical study:  
* We exploited the state-of-the-art DL algorithms and compared their performance using numerous evaluation metrics;
* We identified the most common parameters and examined their influences on the performance of four DL models;
* We highlighted several issues based on the findings from the empirical experiments and recommended possible solutions to address these issues.  
The remainder of this paper is structured as follows. Section 2 provides a short description of four different DL architectures, and reviews previous studies on these algorithms in the phishing detection domain. The methodology used to conduct this research is presented in Section 3, including experiment setup, website features, DL models, and parameter optimization. Section 4 summarizes the findings, highlights the issues observed from the obtained results, and suggests possible solutions for future research directions. Finally, the conclusion and future work of this research is given in Section 5.