Biblio

Website defacements can inflict significant harm on the website owner through the loss of reputation, the loss of money, or the leakage of information. Due to the complexity and diversity of all kinds of web application systems, especially a lack of necessary security maintenance, website defacements increased year by year. In this paper, we focus on detecting whether the website has been defaced by extracting website features and website embedded trojan features. We use three kinds of classification learning algorithms which include Gradient Boosting Decision Tree (GBDT), Random Forest (RF) and Support Vector Machine (SVM) to do the classification experiments, and experimental results show that Support Vector Machine classifier performed better than two other classifiers. It can achieve an overall accuracy of 95%-96% in detecting website defacements.