Cybersecurity meets carbon neutrality: Strategies for sustainable data center security operations

Abstract

Data center security is enhanced with the rapid deployment of AI and ML in cybersecurity, yet energy use and carbon emissions have also increased with this development. In large-scale data center operations, this dual issue emphasizes the need for techniques that advance carbon neutrality while ensuring strong security. This research aims to design a sustainable cybersecurity framework that enhances computational performance while reducing environmental impact through energy-efficient modeling and optimization. This hybrid approach proposed an Oneclass Support Vector Based Bidirectional Snow Geese Algorithm (OSV-Bi-SGA). Carbon aware-cybersecurity traffic datasets are preprocessed through data cleaning, Z-score normalization, and categorical encoding to ensure robust input for modeling. Feature extraction is conducted using principal component analysis (PCA). The proposed OSV-Bi-SGA method is integrated with Bidirectional Long Short-Term Memory (BiLSTM), which captures temporal bidirectional dependencies in traffic sequences. Oneclass support vector machine (OSV) identifies anomalies when only normal class data is available. Snow Geese Algorithm (SGA) enhances parameter optimization, reducing energy cost while maintaining performance. The suggested OSV-Bi-SGA model achieved a high precision (99.42 %), recall (99.24 %), and F1-score (99.32 %), while reducing energy consumption and carbon footprint compared to baseline models. The research demonstrates that integrating evolutionary optimization with deep learning (DL), machine learning (ML) and anomaly detection can balance high-performance cybersecurity with reduced environmental impact. The OSV-Bi-SGA framework provides a promising pathway for sustainable and carbon-neutral data center security operations.