ADAPTING PLC/SCADA SYSTEMS TO MITIGATE INDUSTRIAL IOT CYBERSECURITY RISKS IN GLOBAL MANUFACTURING

Abstract

The convergence of Operational Technology (OT) and Information Technology (IT) in modern industrial infrastructures has revolutionized automation and real-time control, yet it has simultaneously expanded the cybersecurity threat surface of Programmable Logic Controllers (PLCs) and Supervisory Control and Data Acquisition (SCADA) systems. Once confined to isolated networks, these systems now operate within interconnected Industrial Internet of Things (IIoT) ecosystems, facilitating remote accessibility, cloud integration, and cross-enterprise analytics. This integration, while enabling efficiency and predictive maintenance, has also exposed industrial processes to cyber-physical risks that threaten operational safety, reliability, and national infrastructure integrity. The present study addresses these emerging vulnerabilities through the design, implementation, and validation of an adaptive cybersecurity framework for PLC/SCADA architectures, grounded in the ISA/IEC 62443 “zones and conduits” model, NIST SP 800-82 control recommendations, and Zero-Trust Architecture principles. By aligning architectural segmentation, AI-driven anomaly detection, and automated incident response, this research establishes a resilient industrial cybersecurity model tailored to the realities of global manufacturing networks.Experimental validation demonstrated that the proposed framework achieved substantial improvements in resilience and detection performance. Average MTTD was reduced to 2.8 seconds, MTTR averaged 4.8 minutes, and PLOC decreased by over 40% relative to baseline configurations. The hybrid AI model achieved a detection accuracy exceeding 99%, with an ROC-AUC of 0.993, indicating superior precision and reliability compared to conventional rule-based detection systems. The digital twin simulations further confirmed that process stability and communication latency remained within acceptable operational limits (<1%), validating that enhanced cybersecurity did not impede real-time control efficiency. Moreover, automated SOAR responses effectively restored process variables to nominal states within minutes, confirming practical alignment with industrial resilience targets defined in NIST SP 800-160 and IEC 62443-3-3. This research makes significant contributions to the field of industrial cybersecurity by demonstrating a replicable and standards-aligned methodology for integrating AI and architectural defense mechanisms into legacy and modern control systems. The findings provide empirical evidence that adaptive security architectures, supported by AI-driven analytics and digital twin validation, can safeguard industrial control systems against both known and emergent IIoT threats while maintaining deterministic process control. Ultimately, this framework advances the global pursuit of cyber-resilient industrial automation by offering a scientifically validated model that aligns technical, regulatory, and operational objectives for critical infrastructure protection in the age of digital transformation.