An Effective Phishing Detection Model Based on Character Level Convolutional Neural Network from URL

Abstract

Phishing is the easiest way to use cybercrime with the aim of enticing people to give accurate information such as account IDs, bank details, and passwords. This type of cyberattack is usually triggered by emails, instant messages, or phone calls. The existing anti-phishing techniques are mainly based on source code features, which require to scrape the content of web pages, and on third-party services which retard the classification process of phishing URLs. Although the machine learning techniques have lately been used to detect phishing, they require essential manual feature engineering and are not an expert at detecting emerging phishing offenses. Due to the recent rapid development of deep learning techniques, many deep learning-based methods have also been introduced to enhance the classification performance. In this paper, a fast deep learning-based solution model, which uses character-level convolutional neural network (CNN) for phishing detection based on the URL of the website, is proposed. The proposed model does not require the retrieval of target website content or the use of any third-party services. It captures information and sequential patterns of URL strings without requiring a prior knowledge about phishing, and then uses the sequential pattern features for fast classification of the actual URL. For evaluations, comparisons are provided between different traditional machine learning models and deep learning models using various feature sets such as hand-crafted, character embedding, character level TF-IDF, and character level count vectors features. According to the experiments, the proposed model achieved an accuracy of 95.02% on our dataset and an accuracy of 98.58%, 95.46%, and 95.22% on benchmark datasets which outperform the existing phishing URL models.

Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study

“…Similarly, the authors of [24,28,29,32] described the optimization process, but only on certain parameters, for example, the number of convolutional layers, number of kernels, and kernel size. Additionally, in terms of performance metrics, it was observed that accuracy, precision, recall, and F1-score were the most common measures [7,24,28,[30][31][32]34,35,37,38]. Other evaluation metrics were training time, detection time, GPU memory requirement, etc.…”

“…CNN was used as a single classifier in numerous research to distinguish between phishing and legitimate websites [7,8,20,[24][25][26][27][28]. It can also be used in combination with other DL techniques to form an ensemble model and to improve phishing detection accuracy [10,11,[29][30][31][32][33][34][35][36].…”

“…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.…”

Detecting phishing websites using machine learning technique

“…The outcome of this study indicated that the value of true positive was higher rather than the false positive rate. In other study [ 10 ], authors proposed a Convolutional Neural Network (CNN) to detect a phishing URL. In this study, researchers employed a sequential pattern to capture the URL information.…”

“…There is a significant chance of exploitation of user information. For these reasons, phishing in modern society is highly urgent, challenging, and overly critical [ 9 , 10 ]. There have been several recent studies against phishing based on the characteristics of a domain, such as website URLs, website content, incorporating both the website URLs and content, the source code of the website and the screenshot of the website [ 11 ].…”