Green Cybersecurity: Quantifying the Carbon Footprint and Operational Efficiency of Machine-Speed Defense in Autonomous Industrial Control and Smart Manufacturing Systems

Abstract

This research developed a simulation-based framework for quantifying the carbon footprint and operational efficiency of machine-speed cybersecurity defenses in autonomous industrial control systems (ICS) and smart manufacturing environments. Traditional high-energy intrusion detection systems (IDS) relying on intensive machine learning models contribute significantly to carbon emissions, creating an urgent need for green cybersecurity solutions that balance robust threat detection with sustainability. Using publicly available datasets mirroring UNSW-NB15 and CIC-IDS2017, the study modeled and compared lightweight models (Random Forest and Logistic Regression) against an intensive Long Short-Term Memory network. Carbon emissions were tracked with CodeCarbon, while the proposed eco-efficiency index (EEI) integrated F1-score, carbon footprint, and latency to evaluate trade-offs. Results demonstrated that the ultra-light Logistic Regression model achieved up to 91% carbon reduction and ultra-low latency (<3 ms), yielding the highest EEI while satisfying machine-speed requirements for real-time ICS protection. The GreenShield-ICS framework offers a reproducible approach for low-carbon defenses in Industry 4.0, with implications for guiding policy standardization and accelerating industry adoption toward net-zero, energy-efficient cybersecurity practices.