AI-ENHANCED INTRUSION DETECTION FOR INDUSTRY 4.0: A CROSS-REGIONAL STUDY ON MITIGATING ADVANCED PERSISTENT THREATS IN CYBER-PHYSICAL SYSTEMS

Abstract

This study proposes an AI-enhanced Intrusion Detection System (IDS) framework to combat Advanced Persistent Threats (APTs) in Cyber-Physical Systems(CPS)across diverse regional infrastructures. Traditional IDS struggle in resource-constrained environments, with high false positives(72% in Nigeria)and poor adaptability.TheCOVID 19 pandemicworsened vulnerabilities, leaving 68% of manufacturers without real-time OT monitoring. Our solution integrates federated learning (FL) for decentralized training, explainable AI (XAI) for interpretable alerts, and quantum-resistant cryptography for long-term security. This study tackles four challenges namely the 52% energy savings in Africa via 8-bit models, why FL maintains >90% accuracy in low-bandwidth networks, XAI boosts operator trust by 21% in Kenya, and the 96% quantum resilience. Validated across Africa (Kenya), Asia (India), and the West (USA) using real-world datasets (SWaT) and synthetic APTs, the framework achieves 93.2% detection accuracy with a 4.1% false positive rate, outperforming traditional IDS by 27% while reducing bandwidth by 62% and energy use by 42.9%. Field tests in Kenya showed a 35% increase in operator trust due to XAI transparency. Ethical safeguards include differential privacy in FL to protect sensitive data and adherence to ITU-D Ethical AI Guidelines for operator consent in field trials.