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Authors: Balázs Pejó and Nikolett Kapui

Affiliation: ELKH-BME Information Systems Research Group, Laboratory of Cryptography and System Security, Department of Networked Systems and Services, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary

Keyword(s): SQLi, Machine Learning, Data Distribution.

Abstract: Almost 50 years after the invention of SQL, injection attacks are still top-tier vulnerabilities of today’s ICT systems. In this work, we highlight the shortcomings of the previous Machine Learning based results and fill the identified gaps by providing a comprehensive empirical analysis. We cross-validate the trained models by using data from other distributions which was never studied in relation with SQLi. Finally, we validate our findings on a real-world industrial SQLi dataset.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Pejó, B. and Kapui, N. (2023). SQLi Detection with ML: A Data-Source Perspective. In Proceedings of the 20th International Conference on Security and Cryptography - SECRYPT; ISBN 978-989-758-666-8; ISSN 2184-7711, SciTePress, pages 642-648. DOI: 10.5220/0012050100003555

@conference{secrypt23,
author={Balázs Pejó. and Nikolett Kapui.},
title={SQLi Detection with ML: A Data-Source Perspective},
booktitle={Proceedings of the 20th International Conference on Security and Cryptography - SECRYPT},
year={2023},
pages={642-648},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012050100003555},
isbn={978-989-758-666-8},
issn={2184-7711},
}

TY - CONF

JO - Proceedings of the 20th International Conference on Security and Cryptography - SECRYPT
TI - SQLi Detection with ML: A Data-Source Perspective
SN - 978-989-758-666-8
IS - 2184-7711
AU - Pejó, B.
AU - Kapui, N.
PY - 2023
SP - 642
EP - 648
DO - 10.5220/0012050100003555
PB - SciTePress