Title: Security in database management system using machine learning

Authors: M. Deepa; J. Dhilipan

Addresses: Department of Computer Science and Applications, Faculty of Science and Humanities, SRM Institute of Science and Technology, Ramapuram-600089, Chennai, Tamil Nadu, India ' Department of Computer Science and Applications, Faculty of Science and Humanities, SRM Institute of Science and Technology, Ramapuram-600089, Chennai, Tamil Nadu, India

Abstract: The term 'database security' refers to the collection of rules, tools, and processes that have been developed to maintain and protect the databases' confidentiality, integrity, and accessibility. The use of machine learning to improve database management security is becoming more common. The fundamental goal of employing machine learning in security is to make the process of malware detection more actionable, scalable, and successful than conventional techniques, which need the participation of humans. This may be accomplished by making the process more automated. The process entails overcoming problems posed by machine learning, which need to be managed in an effective, logical, and theoretical manner. Machine learning algorithm is applied in the critical paths of the tuner. The optimum configuration for the proposed system yields a throughput boost of between 22% and 35% and a latency reduction of around 60%. The method is robust to various attacks.

Keywords: database security'; security techniques; database threats; integrity; machine learning.

DOI: 10.1504/IJESDF.2024.136024

International Journal of Electronic Security and Digital Forensics, 2024 Vol.16 No.1, pp.124 - 133

Received: 24 Nov 2022
Accepted: 20 Apr 2023
Published online: 12 Jan 2024 *

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