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Authors: Fawaz A. Mereani 1 and Jacob M. Howe 2

Affiliations: 1 City, University of London, Northampton Square, London, U.K., Umm AL-Qura University, Makkah and Saudi Arabia ; 2 City, University of London, Northampton Square, London and U.K.

ISBN: 978-989-758-327-8

Keyword(s): Cascading Classifiers, Stacking Ensemble, Cross-Site Scripting.

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.

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Paper citation in several formats:
Mereani, F. and Howe, J. (2018). Preventing Cross-Site Scripting Attacks by Combining Classifiers.In Proceedings of the 10th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-327-8, pages 135-143. DOI: 10.5220/0006894901350143

@conference{ijcci18,
author={Fawaz A. Mereani. and Jacob M. Howe.},
title={Preventing Cross-Site Scripting Attacks by Combining Classifiers},
booktitle={Proceedings of the 10th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,},
year={2018},
pages={135-143},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006894901350143},
isbn={978-989-758-327-8},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,
TI - Preventing Cross-Site Scripting Attacks by Combining Classifiers
SN - 978-989-758-327-8
AU - Mereani, F.
AU - Howe, J.
PY - 2018
SP - 135
EP - 143
DO - 10.5220/0006894901350143

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