HTMLPhish: Enabling Phishing Web Page Detection by Applying Deep Learning Techniques on HTML Analysis
This addresses the growing problem of phishing attacks for web users by providing a language-independent, client-side detection method, though it is incremental as it applies existing deep learning techniques to HTML analysis.
The paper tackles phishing web page detection by proposing HTMLPhish, a deep learning approach that analyzes HTML content using CNNs to classify pages as phishing or benign, achieving over 93% accuracy and true positive rate on a dataset of over 50,000 HTML documents.
Recently, the development and implementation of phishing attacks require little technical skills and costs. This uprising has led to an ever-growing number of phishing attacks on the World Wide Web. Consequently, proactive techniques to fight phishing attacks have become extremely necessary. In this paper, we propose HTMLPhish, a deep learning based data-driven end-to-end automatic phishing web page classification approach. Specifically, HTMLPhish receives the content of the HTML document of a web page and employs Convolutional Neural Networks (CNNs) to learn the semantic dependencies in the textual contents of the HTML. The CNNs learn appropriate feature representations from the HTML document embeddings without extensive manual feature engineering. Furthermore, our proposed approach of the concatenation of the word and character embeddings allows our model to manage new features and ensure easy extrapolation to test data. We conduct comprehensive experiments on a dataset of more than 50,000 HTML documents that provides a distribution of phishing to benign web pages obtainable in the real-world that yields over 93 percent Accuracy and True Positive Rate. Also, HTMLPhish is a completely language-independent and client-side strategy which can, therefore, conduct web page phishing detection regardless of the textual language.