Visible to the public XSS Attack Detection With Machine Learning and n-Gram Methods

TitleXSS Attack Detection With Machine Learning and n-Gram Methods
Publication TypeConference Paper
Year of Publication2020
AuthorsHabibi, G., Surantha, N.
Conference Name2020 International Conference on Information Management and Technology (ICIMTech)
Date Publishedaug
Keywordsattack vectors, Bayes methods, composability, cookies, Cross Site Scripting, cross-site scripting, detection script, feature extraction, k-nearest neighbour, KNN, learning (artificial intelligence), machine learning, machine learning algorithms, malicious script, malicious scripts, N-gram, n-gram method, naive Bayes, nearest neighbour methods, Predictive Metrics, pubcrawl, Resiliency, security of data, support vector machine, Support vector machines, SVM, Training data, Uniform resource locators, vulnerable, Web sites, website, XSS attack, XSS attack detection, XSS Attacks

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 Naive 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%.

Citation Keyhabibi_xss_2020