Aegis-5: A Hybrid Ensemble Framework for Intrusion Detection in Industry 5.0 Driven Smart Manufacturing Environment

Industry 5.0 represents a transformative paradigm that emphasizes synergy between human expertise, intelligent systems, and hyper connected cyber-physical environments. While this evolution fosters personalized automation and resilient production, it also amplifies the cybersecurity risks inherent in Industrial Internet of Things (IIoT) infrastructures. In this research, we present Aegis-5 a novel adaptive hybrid ensemble framework explicitly designed for intrusion detection in Industry 5.0-enabled smart manufacturing ecosystems. The proposed model integrates five diverse classifiers Random Forest, Gradient Boosting, XGBoost, SVM, and K-Nearest Neighbors using a dynamic weighting strategy guided by per-class precision, recall, and F1-score performance in real time. A meta-learner further synthesizes these predictions to enhance robustness against sophisticated and zero-day attacks. To ensure relevance and reliability, we evaluate the model using two benchmark IIoT datasets: IoT-23 and CIC-IoT 2023, both of which capture a broad spectrum of real-world industrial threats. Experimental results demonstrate that our framework achieves superior performance, with accuracy rates of 99.98% on IoT-23 and 99.95% on CIC-IoT 2023, coupled with precision (99.97%, 99.93%), recall (99.96%, 99.92%), and F1-score (99.96%, 99.93%) respectively., significantly reduces false positives, and adapts effectively to evolving attack behaviors. By aligning intelligent anomaly detection with the responsiveness and adaptability required by Industry 5.0, Aegis-5 offers a scalable, real-time, and practical cybersecurity solution for next-generation industrial systems.