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Preventing Cross-Site Scripting Attacks by Combining Classifiers

Mereani, F. & Howe, J. M. ORCID: 0000-0001-8013-6941 (2018). Preventing Cross-Site Scripting Attacks by Combining Classifiers. In: Proceedings of the 10th International Joint Conference on Computational Intelligence. (pp. 135-143). San Francisco, USA: SCITEPRESS. ISBN 978-989758327-8


Cross-Site Scripting (XSS) is one of the most popular attacks targeting web applications. Using XSS attackers can obtain sensitive information or obtain unauthorized privileges. This motivates building a system that can recognise a malicious script when the attacker attempts to store it on a server, preventing the XSS attack. This work uses machine learning to power such a system. The system is based on a combination of classifiers, using cascading to build a two phase classifier and the stacking ensemble technique to improve accuracy. The system is evaluated and shown to achieve high accuracy and high detection rate on a large real world dataset.

Publication Type: Conference or Workshop Item (Paper)
Publisher Keywords: Cascading Classifiers; Stacking Ensemble; Cross-Site Scripting
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology > Computer Science
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