Schneider, Lukas and Fischer, Hannah and Becker, Jonas (2025) DESIGN OF AI-POWERED CYBERSECURITY THREAT DETECTION SYSTEMS TO PROTECT BUSINESS NETWORKS AND DIGITAL INFRASTRUCTURE FROM EMERGING CYBER RISKS. International Journal of Business, Law and Political Science, 2 (12). pp. 666-673. ISSN 3032-1298

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Abstract

Objective: This paper presents the design and implementation of an AI-powered cybersecurity threat detection system that leverages deep learning and behavioral analysis to identify and mitigate emerging cyber risks. Method: Our proposed architecture combines convolutional neural networks for malware detection, recurrent neural networks for anomaly detection in network traffic, and reinforcement learning for adaptive threat response. Results: Evaluation on benchmark datasets and real-world deployment scenarios demonstrates a threat detection accuracy of 99.2% with an average response time of 45 milliseconds. The system effectively addresses zero-day attacks and advanced persistent threats, providing robust protection for enterprise digital assets. Novelty: The evolving landscape of cyber threats poses significant challenges to business networks and digital infrastructure worldwide.

Item Type:	Article
Subjects:	A General Works > AI Indexes (General)
Depositing User:	admin eprints
Date Deposited:	07 May 2026 09:28
Last Modified:	07 May 2026 09:28
URI:	http://eprints.umsida.ac.id/id/eprint/16460

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