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

TL;DR

This paper presents an eco-aware anomaly detection framework that integrates machine learning with real-time carbon and energy metrics, demonstrating environmentally responsible cybersecurity with maintained detection performance.

Contribution

It introduces a novel framework combining cybersecurity anomaly detection with environmental impact metrics, enabling sustainable AI practices in data centers.

Findings

Random Forest and Logistic Regression achieve high eco efficiency.

Energy consumption reduced by over 40% with minimal accuracy loss.

PCA decreases computational load with negligible recall impact.

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.