Biblio

Cross-Site Scripting (XSS) is an attack most often carried out by attackers to attack a website by inserting malicious scripts into a website. This attack will take the user to a webpage that has been specifically designed to retrieve user sessions and cookies. Nearly 68% of websites are vulnerable to XSS attacks. In this study, the authors conducted a study by evaluating several machine learning methods, namely Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Naïve Bayes (NB). The machine learning algorithm is then equipped with the n-gram method to each script feature to improve the detection performance of XSS attacks. The simulation results show that the SVM and n-gram method achieves the highest accuracy with 98%.

Cross site scripting (XSS) is a kind of common attack nowadays. The attack patterns with the new technical like HTML5 that makes detection task getting harder and harder. In this paper, we focus on the browser detection mechanism integrated with HTML5 and CORS properties to detect XSS attacks with the rule based filter by using browser extensions. Further, we also present a model of composition pattern estimation system which can be used to judge whether the intercepted request has malicious attempts or not. The experimental results show that our approach can reach high detection rate by tuning our system through some frequently used attack sentences and testing it with the popular tool-kits: XSSer developed by OWASP.