AI-Driven Secure Smart Manufacturing: Integrating Database Indexing, Industrial Cybersecurity, And Real-Time IIoT Analytics in Wireless Automation Architectures

Abstract

The rapid evolution of Industry 4.0 has propelled smart manufacturing into an era of AI-driven intelligence, requiring seamless integration of cybersecurity, data analytics, and real-time control within wireless architectures. This study proposes a comprehensive framework that integrates artificial intelligence, intelligent database indexing, industrial cybersecurity, and real-time IIoT analytics for secure and scalable smart manufacturing systems. An experimental simulation was conducted using AI models including LSTM, Random Forest, and autoencoders to optimize predictive maintenance and anomaly detection. Indexing strategies, B-Tree, Hash, and AI-adaptive were evaluated for query latency and data throughput, while wireless protocols such as Zigbee, Wi-Fi 6, and private 5G were assessed for latency, packet loss, and encryption overhead. Results indicate that AI-adaptive indexing achieved the lowest query latency (10 ms) and highest throughput (3,200 QPS), while LSTM delivered superior predictive accuracy (F1 score 95.1%) and autoencoders demonstrated robust anomaly detection (97.5% accuracy, 2.3% false-positive rate). Private 5G emerged as the most reliable wireless medium with minimal latency (7 ms) and the highest data integrity. The integrated approach demonstrates strong statistical significance and operational viability, highlighting the potential of AI-driven solutions in enhancing resilience, efficiency, and security in next-generation smart manufacturing ecosystems.