AI-Powered Predictive Models for Smart Factory Cyber-Physical Threat Mitigation

Abstract

Industry 4.0 smart factories enhance output with integrated systems and current technologies. Companies using cyber-physical systems (CPS) face multifaceted threats including cyberattacks and physical flaws. Traditional mitigation methods fail because these hazards are dynamic. We use AI-powered prediction models to mitigate smart industrial cyber-physical threats. These models use machine learning and data-driven analytics to prevent security breaches, system breakdowns, and operational abnormalities. The research examines AI's function in CPS anomaly detection, predictive maintenance, and real-time threat response. Investigations include data privacy, scalability, and implementation. Real-world case studies demonstrate how these approaches may enhance smart manufacturing security.