Towards eco friendly cybersecurity: machine learning based anomaly detection with carbon and energy metrics

2025-11-03

International Journal of Applied Mathematics

AI Summary

This study addresses a critical gap in cybersecurity research by incorporating energy consumption and carbon emissions into the evaluation of AI-based anomaly detection systems, an aspect largely overlooked in existing literature. The authors propose the first green framework that integrates environmental impact metrics into network intrusion detection assessment, introducing an "Eco-Efficiency Index" to jointly quantify model performance and ecological cost. Leveraging Logistic Regression, Random Forest, SVM, Isolation Forest, and XGBoost, the framework employs CodeCarbon for carbon tracking and principal component analysis for energy-efficiency optimization. Experimental results demonstrate that the optimized Random Forest and lightweight Logistic Regression models achieve high detection accuracy while reducing energy consumption by over 40% compared to XGBoost, thereby substantiating the feasibility of environmentally sustainable cybersecurity solutions.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Philosophy and Ethics of AI: Privacy & Security

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

The rising energy footprint of artificial intelligence has become a measurable component of U.S. data-center emissions, yet cybersecurity research seldom considers its environmental cost. This study introduces an eco-aware anomaly detection framework that unifies machine learning–based network monitoring with real-time carbon and energy tracking. Using the publicly available Carbon-Aware Cybersecurity Traffic Dataset comprising 2,300 flow-level observations, we benchmark Logistic Regression, Random Forest, Support Vector Machine, Isolation Forest, and XGBoost models across energy, carbon, and performance dimensions. Each experiment is executed in a controlled Colab environment instrumented with the CodeCarbon toolkit to quantify power draw and equivalent CO₂ output during both training and inference. We construct an Eco-Efficiency Index that expresses F1-score per kilowatt-hour to capture the trade-off between detection quality and environmental impact. Results reveal that optimized Random Forest and lightweight Logistic Regression models achieve the highest eco-efficiency, reducing energy consumption by more than 40% compared to XGBoost while sustaining competitive detection accuracy. Principal Component Analysis further decreases computational load with negligible loss in recall. Collectively, these findings establish that integrating carbon and energy metrics into cybersecurity workflows enables environmentally responsible machine learning without compromising operational protection. The proposed framework offers a reproducible path toward sustainable, carbon-accountable cybersecurity aligned with emerging U.S. green computing and federal energy-efficiency initiatives

Problem

• cybersecurity
• energy consumption
• carbon emissions
• anomaly detection
• sustainability

Innovation

• eco-aware anomaly detection
• carbon-aware machine learning
• energy-efficient cybersecurity
• Eco Efficiency Index
• CodeCarbon