# Detecting Phishing URLs Based on a Deep Learning Approach to Prevent Cyber-Attacks
###### Abstract  
Phishing is one of the most widely observed types of internet cyber-attack, through which hundreds of clients using different internet services are targeted every day through different replicated websites. The phishing attacker spreads messages containing false URL links through emails, social media platforms, or messages, targeting people to steal sensitive data like credentials. Attackers generate phishing URLs that resemble those of legitimate websites to gain these confidential data. Hence, there is a need to prevent the siphoning of data through the duplication of trustworthy websites and raise public awareness of such practices. For this purpose, many machine learning and deep learning models have been employed to detect and prevent phishing attacks, but due to the ever-evolving nature of these attacks, many systems fail to provide accurate results. In this study, we propose a deep learning-based system using a 1D convolutional neural network to detect phishing URLs. The experimental work was performed using datasets from Phish-Tank, UNB, and Alexa, which successfully generated 200 thousand phishing URLs and 200 thousand legitimate URLs. The experimental results show that the proposed system achieved 99.7% accuracy, which was better than the traditional models proposed for URL-based phishing detection. phishing; deep learning; convolutional neural network; deep neural network +
Footnote †: journal: Applied sciences  
Phishing websites are nearly identical to actual websites. Scammers, without any margin of error, duplicate legitimate websites, making differences between the real and copy websites indistinguishable from the naked eye. The scammers make sure that the interface and user functionalities provided by the phishing websites are identical to those provided by the legitimate websites [5]. The Anti-Phishing Working Group (APWG) has generated a report showing that phishing attacks in the fourth quarter of 2023 involved 1,077,501 sites. It observed that there were almost five million phishing attacks in 2023, the highest recorded year for phishing on record [6]. In the fourth quarter of 2023, according to the APWG, the industries most frequently targeted were those shown in Figure 2. It is clear that the number of phishing attacks has increased significantly, and there is a need to take crucial steps to prevent these attacks by using efficient approaches. The demand for effective countermeasures has made phishing detection an important area of research in the past two decades, from which different categories of phishing detection have emerged. The main categories of phishing detection are as follows: (a) blacklist- and whitelist-based approaches; (b) web-page-based visual similarity; and (c) URL- and web-content-based feature extraction. Many machine learning- and deep learning-based approaches have been employed to extract URL- and web-based features to improve the detection of phishing attacks [7,8]. These approaches have also been used to improve the performance of detection systems. Figure 1: An overview of phishing websites. Figure 2: The industries most targeted by phishing attacks in 2023. Due to the varying limitations of these studies and the continuous evolution of phishing attacks, no proposed approach has been sufficient to provide a comprehensive mechanism that detects and prevents phishing attacks. By using a deep-learning-based approach, this study proposes a one-dimensional convolutional neural network (1D CNN) for highly robust phishing URL detection. The 1D CNN is low-cost and well-suited to real-world scenarios, as it has a shallow architecture that can learn challenging tasks quickly and as it can run on any common CPU and hardware. However, 1D CNNs sometimes struggle to capture complex hierarchical features compared with other architectures like recurrent neural networks (RNNs) or transformers, especially when the data contain long-range dependencies. Accordingly, the study applies a system that uses URL-based feature extraction to detect phishing attacks.