Phishing webpage detection based on global and local visual similarity

Abstract

In recent years, phishing websites have constantly evolved, causing traditional URL or HTML-based detection methods less effective. This limitation motivated the development of visual similarity-based detection methods. These methods can identify phishing websites in a rapidly changing phishing landscape since the key factor in deceiving users is the high visual similarity between phishing pages and their corresponding legitimate ones. However, existing similarity-based methods often suffer from high false positive rates. To alleviate the problem, this paper proposed a new webpage similarity phishing detection framework. Using a deep learning model, the framework locates a website logo and integrates the logo area with the full webpage to detect phishing pages. Experimental evaluations on multiple datasets demonstrate the model’s ability to achieve a precision of 91% and a recall of 88% in detecting phishing websites. Comparative experiments against existing methods reveal that the proposed model enhances precision and recall by 17% and 21%, respectively, while reducing the false positive rate by 1%.