Triplet Mining-based Phishing Webpage Detection

Abstract

Phishing web pages impersonate legitimate websites to trick users into entering sensitive information such as their credentials. In many high profile data breaches, the initial entry points have been traced back to phishing attacks. Attackers are using increasingly sophisticated methods such as code obfuscation to bypass existing phishing detection systems. Since phishing websites show very high visual similarity to the respective target pages, recent advances in Convolutional Neural Networks (CNN) can be leveraged to build better phishing detection systems. In this work, we propose a novel CNN architecture consisting of two paths to capture the content similarity and structural similarity between web pages. Leveraging the fact that web pages of the same web site are visually similar, we use triplet learning to train our model without any labelled phishing examples.