Title: Optimisation of IP artificial intelligence network in information learning and computer database security monitoring system

Authors: Yaming Kang; Tingting Wu; Jun Li

Addresses: School of Information Engineering, Yulin University, Yulin 719000, Shaanxi, China ' Shandong Vocational College of Science and Technology, Weifang 261053, Shandong, China ' Shandong Vocational College of Economics and Trade, Weifang 261011, Shandong, China

Abstract: In the present scenario, the security of computer databases is of utmost importance in the technological environment. The growing frequency and complexity of attacks create substantial risks to the security breaches of these databases, especially in artificial intelligence networks. To tackle these challenges, it is crucial to prioritise integrating artificial intelligence networks based on intellectual property. The present research introduces the optimised database security monitoring system (ODSMS) as a complete solution to optimise the risks of security breaches. The ODSMS system integrates intelligent dynamic malware detection, which leverages machine learning techniques with pre-processing, dynamic analysis, and malware family categorisation to optimise the complexity of security breaches. The system utilises optimal feature selection via genetic algorithm (GA), query modelling using convolutional neural networks (CNN), and risk assessment, including learning and detection stages to optimise artificial intelligence networks.

Keywords: database security; convolutional neural networks; CNNs; artificial intelligence; information learning.

DOI: 10.1504/IJIIDS.2025.147446

International Journal of Intelligent Information and Database Systems, 2025 Vol.17 No.3/4, pp.570 - 594

Received: 05 Mar 2024
Accepted: 02 Jul 2024
Published online: 15 Jul 2025 *

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