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

Published in: ACM Transactions on Autonomous and Adaptive Systems, 2026

DOI: 10.1145/3787224

Authors

• Vijay Govindarajan — Colorado State University, Fort Collins, CO, USA
• Faraz Ahmed — Crisp Technologies LLC, USA
• Zaid Bin Faheem — Wuhan University, Wuhan, China
• Muhammad Bilal — Rawalpindi Women University, Pakistan
• Manel Ayadi — Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
• Jehad Ali — Ajou University, Suwon, South Korea

Abstract

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.

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.

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.

Key Contributions

• Dynamic Hybrid Ensemble Framework integrating five classifiers with real-time adaptive weighting
• Meta-learning via Logistic Regression with hybrid soft/hard voting for robustness against zero-day attacks
• Evaluation on real-world IIoT datasets (IoT-23 and CIC-IoT 2023) with advanced preprocessing
• State-of-the-art detection accuracy (99.98% on IoT-23, 99.94% on CIC-IoT 2023)
• Scalable, latency-sensitive solution aligned with Industry 5.0 cyber-physical requirements

Implementation

This repository includes a Python implementation of the Aegis-5 framework.

Setup

pip install -r requirements.txt

Usage

from aegis5 import Aegis5

model = Aegis5(
    confidence_threshold=0.95,  # tau for hybrid voting
    beta=2.0,                   # temperature for dynamic weighting
    use_feature_selection=True, # ANOVA F-test + RFECV
    use_pca=True,               # PCA dimensionality reduction
    use_smote=True              # SMOTE for class imbalance
)

model.fit(X_train, y_train)
results = model.evaluate(X_test, y_test)

Demo

Run the demo with a synthetic IIoT dataset:

python demo.py

Architecture

• Base Classifiers: Random Forest, Gradient Boosting, XGBoost, SVM, KNN (tuned hyperparameters from Table 4)
• Dynamic Weighting: Per-class softmax over F1-scores in a sliding window (Eq. 2, K=1000, β=2.0)
• Meta-Learner: Logistic Regression synthesizing weighted base classifier probabilities
• Hybrid Voting: Soft voting when meta-learner confidence ≥ τ (0.95), hard voting otherwise (Algorithm 1)
• Preprocessing: Median imputation → StandardScaler → ANOVA F-test + RFECV → PCA → SMOTE

Keywords

Industry 5.0, Smart Manufacturing, Hybrid Ensemble Framework, Real-Time Threat Detection, IIoT Security, Cyber-Physical Systems, Adversarial Training, Meta-Learning

Citation

@article{govindarajan2026aegis5,
  title={Aegis-5: A Hybrid Ensemble Framework for Intrusion Detection in Industry 5.0 Driven Smart Manufacturing Environment},
  author={Govindarajan, Vijay and Ahmed, Faraz and Faheem, Zaid Bin and Bilal, Muhammad and Ayadi, Manel and Ali, Jehad},
  journal={ACM Transactions on Autonomous and Adaptive Systems},
  year={2026},
  doi={10.1145/3787224},
  publisher={ACM}
}

Paper

The full paper is available at ACM Digital Library.