Green-aware intrusion detection systems: A sustainability-driven approach to network security

Abdullah Alshammari

PLOS ONE, 2026, vol. 21, issue 10, 1-34

Abstract: The rapid proliferation of Internet of Things (IoT) devices has intensified network security challenges. Simultaneously, growing computational demands raise critical concerns about energy consumption and environmental sustainability. This paper proposes a Green-Aware Intrusion Detection System (GA-IDS) integrating sustainability metrics with machine learning to balance detection performance and energy efficiency. The framework employs a lightweight convolutional neural network optimized for resource-constrained platforms with dynamic power management and adaptive computational scaling. A novel carbon-conscious scheduling algorithm dynamically adjusts model complexity based on real-time grid carbon intensity. Experimental evaluation on CICIDS2017, NSL-KDD, and ToN-IoT datasets demonstrates 98.7% detection accuracy with 43% energy reduction compared to traditional deep learning-based IDS. The carbon-aware scheduler reduces emissions by 58.7% during high-carbon grid conditions without compromising detection quality. Comprehensive ablation studies validate component contributions, and comparative analysis against ten state-of-the-art approaches confirms GA-IDS achieves the highest Green Efficiency Score (0.0796), demonstrating superior balance between security performance and environmental responsibility.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0355453

DOI: 10.1371/journal.pone.0355453

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