Web Phishing Detection Using a Deep Learning Framework

Ping Yi,Yuxiang Guan,Ting Zhu,Wei Wang,Futai Zou,Yao Yao

doi:10.1155/2018/4678746

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.

Highlights

Web service is a communication protocol and software between two electronic devices over the Internet [1]
From a technical point of view, the detection of phishing generally includes the following categories: detection based on a black list [10] and white list, detection based on Uniform Resource Locator (URL) features [11], detection based on web content, and detection based on machine learning
Since the entire Deep Belief Networks (DBN) can be seen as a feature extraction process, the output of the top Restricted Boltzmann Machine (RBM) can be seen as a feature in a space

Summary

Introduction

Web service is a communication protocol and software between two electronic devices over the Internet [1]. From a technical point of view, the detection of phishing generally includes the following categories: detection based on a black list [10] and white list, detection based on Uniform Resource Locator (URL) features [11], detection based on web content, and detection based on machine learning. (ii) We introduce DBN to detect web phishing. We discuss the training process of DBN and get the appropriate parameters to detect web phishing. (iii) We use real IP flows data from ISP to evaluate the effectiveness of the detection model on DBN.

Related Works

The Phishing Detection Model Based on DBN

Test and Analysis

Findings

Conclusions