AI-Driven Secure Smart Manufacturing: Integrating Database Indexing and Industrial Cybersecurity in Wireless Architectures

Abstract

The rapid evolution of Industry 4.0 has brought forth the need for intelligent, secure, and efficient smart manufacturing systems. This study proposes an integrated framework that leverages artificial intelligence (AI), database indexing, and industrial cybersecurity within wireless communication architectures to enable real-time, secure, and scalable manufacturing operations. AI models including Random Forest and Autoencoders were trained to detect cybersecurity threats with high accuracy, achieving up to 96.5% accuracy and strong F1-scores. Simultaneously, advanced indexing techniques such as Hash and B-Tree structures optimized query performance and minimized latency in high-throughput data environments. To secure wireless architectures, layered security protocols including AES-256 encryption, WPA3, and blockchain-based access control were evaluated for their effectiveness in intrusion detection and latency trade-offs. The proposed framework was validated through simulation, empirical analysis, and statistical testing, including ANOVA and ROC curve analysis. Results confirmed the statistical significance of performance variations across indexing methods and security models. This multi-layered architecture demonstrates that the integration of AI, efficient data management, and strong wireless cybersecurity not only enhances operational resilience but also enables real-time responsiveness in autonomous industrial systems. The findings offer a scalable, secure blueprint for manufacturers seeking to implement AI-driven smart factories in alignment with Industry 4.0 initiatives.