Green Cyber security: Designing Low-Energy, Carbon-Aware Threat Detection Frameworks

Abstract

This study explored the design and implementation of a low-energy, carbon-aware threat detection framework aimed at enhancing sustainability in cyber security operations. The research focused on developing a Green Hybrid Intrusion Detection System (IDS) that combined energy-efficient machine learning algorithms with carbon-intensity-based scheduling to minimize environmental impact while maintaining high detection accuracy. Quantitative analyses demonstrated substantial reductions in energy consumption and carbon emissions, achieving up to 37.6% energy savings compared to conventional models. The proposed framework also recorded superior performance metrics, including a 95.3% detection accuracy and a 3.8% false positive rate, highlighting its efficiency and reliability. The findings indicated that incorporating environmental awareness into cyber security systems could yield dual benefits—reducing the carbon footprint of data protection processes and improving operational responsiveness. Moreover, the study established that sustainability and cybersecurity goals are not mutually exclusive but can coexist through optimized computation and adaptive threat management. These results provide a foundation for rethinking cybersecurity architectures in alignment with global sustainability targets. The study further recommended the adoption of energy-aware design principles in cyber security governance, the establishment of green security standards, and expanded real-world testing in cloud, IoT, and industrial networks to ensure scalability and resilience of eco-efficient cyber security frameworks.