A unified low-carbon cybersecurity framework integrating energy-efficient intrusion detection, lightweight...

The rapid expansion of edge-cloud computing infrastructures has intensified both cybersecurity demands and the associated energy consumption and carbon footprint of intrusion detection systems (IDS). This paper presents GreenShield, a unified low-carbon cybersecurity framework that integrates energy-efficient deep learning-based intrusion detection with knowledge distillation and dynamic quantization, ASCON lightweight cryptography, hierarchical federated learning with gradient compression, and a carbon-aware scheduling engine across distributed edge-fog-cloud architectures. GreenShield employs a threat-adaptive quantization mechanism that scales model precision (4-32 bit) based on real-time threat levels and a carbon-conscious scheduling controller that dynamically aligns security workload execution with renewable energy availability forecasts. Extensive experiments on the UNSW-NB15 and CIC-IDS2017 datasets demonstrate that GreenShield achieves 98.73% detection accuracy with 67.4% energy reduction compared to conventional deep learning-based IDS, while reducing operational carbon emissions by up to 97.6% (equivalent to approximately 2.8 kg CO₂-eq per hour savings in a typical edge deployment). The hierarchical federated learning architecture reduces communication overhead by 58.2% through Top-k gradient sparsification, and the dynamic quantization mechanism achieves 71.3% inference energy reduction during low-threat periods. These results establish GreenShield as a viable, scalable solution for sustainable cybersecurity that supports carbon-conscious security workflows in next-generation edge-cloud computing environments.