XSS Attack Detection With Machine Learning and n-Gram Methods

Gulit Habibi, Nico Surantha

202027 citationsDOI

Abstract

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%.

Topics & Concepts

Cross-site scriptingComputer scienceSupport vector machineScripting languageNaive Bayes classifierMachine learningArtificial intelligencePhishingn-gramWeb pageComputer securityWorld Wide WebOperating systemThe InternetWeb application securityWeb developmentLanguage modelWeb Application Security VulnerabilitiesAdvanced Malware Detection TechniquesSpam and Phishing Detection