Web Phishing Detection Using a Deep Learning Framework

TLDR

Web services are essential Internet communication tools, yet they face phishing attacks that impersonate legitimate entities to steal credentials and cause information disclosure and property damage. The study aims to apply a deep learning framework for detecting phishing websites. The authors design original and interaction features and build a Deep Belief Network model to detect phishing sites. Evaluation on real ISP IP flows shows the DBN model achieves about 90 % true‑positive rate with a 0.6 % false‑positive rate.

Abstract

Web service is one of the key communications software services for the Internet. Web phishing is one of many security threats to web services on the Internet. Web phishing aims to steal private information, such as usernames, passwords, and credit card details, by way of impersonating a legitimate entity. It will lead to information disclosure and property damage. This paper mainly focuses on applying a deep learning framework to detect phishing websites. This paper first designs two types of features for web phishing: original features and interaction features. A detection model based on Deep Belief Networks (DBN) is then presented. The test using real IP flows from ISP (Internet Service Provider) shows that the detecting model based on DBN can achieve an approximately 90% true positive rate and 0.6% false positive rate.