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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

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1 Papers

SQLi Detection with ML: A Data-Source Perspective

Topics: Web Security and Privacy; Reliability and Dependability; Machine Learning and AI Security; Security usability

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

Digital Object Identifier: 10.5220/0012050100003555

Proceedings of SECRYPT 2023, ISBN: 978-989-758-666-8

Quick 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.