Towards eco friendly cybersecurity: machine learning based anomaly detection with carbon and energy metrics
KC Aashish Md Zakir Hossain Zamil Md Shafiqul Islam Mridul Lamia Akter Farmina Sharmin Eftekhar Hossain Ayon Md Maruf Bin Reza Ali Hassan Abdur Rahim Sirapa Malla
作者信息
Abstract
The rising energy footprint of artificial intelligence has become a measurable component of US 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 2300 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 forty percent 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 US green computing and federal energy efficiency initiatives.引用本文复制引用
KC Aashish,Md Zakir Hossain Zamil,Md Shafiqul Islam Mridul,Lamia Akter,Farmina Sharmin,Eftekhar Hossain Ayon,Md Maruf Bin Reza,Ali Hassan,Abdur Rahim,Sirapa Malla.Towards eco friendly cybersecurity: machine learning based anomaly detection with carbon and energy metrics[EB/OL].(2025-12-31)[2026-05-08].https://arxiv.org/abs/2601.00893.学科分类
环境科学理论/环境保护宣传、环境保护教育/环境科学技术现状/环境保护组织、环境保护会议/计算技术、计算机技术
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