A Multi-layer Model for Website Defacement Detection
Xuan Dau Hoang, Ngoc Tuong Nguyen · 2019
Website defacements have long been considered one of major threats to websites and web portals of enterprises and government organizations. Defacement attacks can bring in serious consequences to website owners, including immediate interruption of website operations and damage of the owner reputation, which may lead huge financial losses. Many solutions have been researched and deployed for monitoring and detection of defacement attacks, such as those based on checksum comparison, diff comparison, DOM tree analysis and advanced methods. However, some solutions only work on static web pages and some others demand extensive computing resources. This paper proposes a multi-layer model for website defacement detection. The proposed model is based on three layers of machine learning-based detection for web text content and a layer for checking the integrity of embedded images in the web pages. Our experiments show that the proposed model produces the overall detection accuracy of more than 98.8% and the false positive rate of less than 1.04% for all tested cases.