A Website Defacement Detection Method Based on Machine Learning Techniques

Abstract

Website defacement attacks have been one of major threats to websites and web portals of private and public organizations. The attacks can cause serious consequences to website owners, including interrupting the website operations and damaging the owner's reputation, which may lead to big financial losses. A number of techniques have been proposed for website defacement monitoring and detection, such as checksum comparison, diff comparison, DOM tree analysis and complex algorithms. However, some of them only work on static web pages and the others require extensive computational resources. In this paper, we propose a machine learning-based method for website defacement detection. In our method, machine learning techniques are used to build classifiers (detection profile) for page classification into either Normal or Attacked class. As the detection profile can be learned from training data, our method can work well for both static and dynamic web pages. Experimental results show that our approach achieves high detection accuracy of over 93% and low false positive rate of less than 1%. In addition, our method does not require extensive computational resources, so it is practical for online deployment.