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.
International Joint Conference on Computational Intelligence, 18-20 Sep 2018, Seville, Spain.
Abstract
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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