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.

翻译：人工智能日益增长的能源足迹已成为美国数据中心排放的可测量组成部分，然而网络安全研究很少考虑其环境成本。本研究提出了一种生态感知异常检测框架，将基于机器学习的网络监控与实时碳排及能源追踪相结合。利用包含2300条流级观测数据的公开碳感知网络安全流量数据集，我们在能源消耗、碳排放和性能表现三个维度上对逻辑回归、随机森林、支持向量机、隔离森林和XGBoost模型进行了基准测试。每个实验均在配备CodeCarbon工具包的受控Colab环境中执行，以量化训练和推理阶段的功耗及等效二氧化碳输出。我们构建了生态效率指数，通过每千瓦时对应的F1分数来表征检测质量与环境影响之间的权衡关系。结果显示，经过优化的随机森林模型和轻量级逻辑回归模型实现了最高的生态效率，与XGBoost相比在保持竞争力的检测准确率的同时，能耗降低超过百分之四十。主成分分析进一步降低了计算负载，而召回率损失可忽略不计。综合而言，这些发现表明将碳排和能源指标整合到网络安全工作流程中，能够在保障运营防护能力的同时实现环境友好的机器学习。所提出的框架为构建可持续的碳责任型网络安全体系提供了可复现的路径，符合美国新兴的绿色计算和联邦能效倡议。