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