A CNN-Based Model for Detecting Website Defacements

Abstract

Over last decade, defacement attacks to websites and web applications have been considered a critical threat in many private and public organizations. A defacement attack can result in a severe effect to the owner’s website, such as instant discontinuity of website operations and damage of the owner’s fame, which in turn may lead to big financial damages. Many solutions have been studied and deployed for monitoring and detecting defacement attacks, such as those based on simple comparison methods and those based on complicated methods. However, some solutions only work on static web-pages and some others can work on dynamic web-pages, but they generate high level of false alarms. This paper proposes a Convolutional Neural Network (CNN)-based detection model for website defacements. The model is an extension of previous models based on traditional supervised machine learning techniques and its aims are to improve the detection rate and reduce the false alarm rate. Experiments conducted on the dataset of 100,000 web-pages show that the proposed model performs significantly better than models based on traditional supervised machine learning.