# Detecting Phishing URLs Based on a Deep Learning Approach to Prevent Cyber-Attacks
## 2 Literature Review
### Traditional Methods
#### 2.1.5 URL-Based Methods  
Several approaches based on machine learning and deep learning to detect URL-based phishing attacks are discussed below. Machine Learning-Based Methods  
In this study, an approach based on the survey and structural detection of malware-based URL detection techniques using machine learning is proposed. By detecting malicious URLs using different machine learning techniques like feature extraction, feature representation, algorithm design, blacklisting, and heuristic approaches are used to detect malicious URLs and binary classification is applied to classify the results as \"malicious\" or \"benign\". Machine learning aims to maximize the prediction accuracy via feature representation followed by classification, using a data-driven optimization approach [22]. In this study, 19,066 phishing attacks were monitored, and over 90% of those attacks were real. Several techniques were used against the phishing attacks, in which quick and efficient detection was achieved using the prevention tool. A phishing detection website was created via a hierarchical clustering approach in which vector numbers of tags were grouped; these numbers were generated from the DOMs of attacked websites according to their proportional distances. Machine learning algorithms were used for the classification of the detected websites [23]. A survey was conducted on the phishing detection approaches examined in this study and malicious URL detection techniques using machine learning. Many formulations have been provided for the detection task, and by utilizing machine learning models, the study identified the categorization, contributions, and different problems that caused a gap in malicious URL detection. Furthermore, the article reviewed a timely and comprehensive range of studies that are also helpful for practitioners in the cyber-security industry. Moreover, much open research and many open-ended challenges to work on in the future were mentioned [24]. Elsewhere, a real-time anti-phishing tool system was proposed for the detection of malicious URLs in a study that used seven different classification algorithms and features based on natural language processing (NLP). That work was based on previous studies. The study proposed a different system, which was language-independent, using a huge amount of phishing and legitimate data and real item execution from the detection of websites. Feature-rich machine learning classifiers were used for independent servicer detection. A random forest algorithm with NLP-based features was used for the evolution of performance, with a 97.89% accuracy rate [8]. In another case, in a study on the detection of phishing URLs, the researchers adopted a distributed representation of words using a given URL. Seven different machine learning algorithms were used for the prediction of whether websites were phishing sites or not. The algorithms provided a satisfactory performance level, beyond that of past studies. Unseen characters were identified, and loss of semantic information was detected using a bag-of-words model, which retained semantic information [25]. In another study, an anti-phishing protection system was introduced that consisted of an email web browser extension and a machine learning-based phishing detection server. The browser extension was used to extract the URL, capture the screenshot, and store the user visiting history as a profile on the client side. Phishing detection was performed in the following steps: (a) using a machine learning model to predict whether the URL was phishing or not based on 13 different features, (b) using blacklisting and whitelisting on a third-party server to filter the new URL, and (c) analyzing website logs using computer vision technology to detect and compare the similarity of screenshots of web pages. The results showed that the model achieved 95% accuracy using a random forest classifier [26]. Beyond this, another study presented a machine-learning-based browser extension for the detection of URL-based phishing.