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

• URL: http://arxiv.org/abs/2601.00893v1
• Date: Wed, 31 Dec 2025 14:36:57 GMT
• Title: Towards eco friendly cybersecurity: machine learning based anomaly detection with carbon and energy metrics
• Authors: 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,
• Abstract summary: This study introduces an eco aware anomaly detection framework that unifies machine learning based network monitoring with real time carbon and energy tracking.<n>We benchmark Logistic Regression, Random Forest, Support Vector Machine, Isolation Forest, and XGBoost models across energy, carbon, and performance dimensions.<n>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.
• Score: 0.17476892297485447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• AI-CARE: Carbon-Aware Reporting Evaluation Metric for AI Models [2.7946918847372277]
  We propose AI-CARE, an evaluation tool for reporting energy consumption, and carbon emissions of machine learning models.<n>We demonstrate, through theoretical analysis and empirical validation, that carbon-aware benchmarking changes the relative ranking of models.<n>Our proposal aims to shift the research community toward transparent, multi-objective evaluation and align ML progress with global sustainability goals.
  arXiv  Detail & Related papers  (2026-02-17T21:52:48Z)
• Lightweight Autoencoder-Isolation Forest Anomaly Detection for Green IoT Edge Gateways [5.113469590433218]
  Conventional anomaly detection schemes often prioritize accuracy while overlooking computational efficiency and environmental impact.<n>This paper presents textitEcoDefender,' a sustainable hybrid anomaly detection framework that integrates representation learning with anomaly scoring.<n>Experiments on realistic IoT traffic confirm these theoretical insights, achieving up to 94% detection accuracy with an average CPU usage of only 22%, 27 ms inference latency, and 30% lower energy consumption compared to AE-only baselines.
  arXiv  Detail & Related papers  (2025-11-23T00:48:34Z)
• Diffusion-Modeled Reinforcement Learning for Carbon and Risk-Aware Microgrid Optimization [48.70916202664808]
  DiffCarl is a diffusion-modeled carbon- and risk-aware reinforcement learning algorithm for intelligent operation of multi-microgrid systems.<n>It outperforms classic algorithms and state-of-the-art DRL solutions, with 2.3-30.1% lower operational cost.<n>It also achieves 28.7% lower carbon emissions than those of its carbon-unaware variant and reduces performance variability.
  arXiv  Detail & Related papers  (2025-07-22T03:27:07Z)
• CarboFormer: A Lightweight Semantic Segmentation Architecture for Efficient Carbon Dioxide Detection Using Optical Gas Imaging [4.567122178196833]
  Carbon dioxide (CO$$) emissions are critical indicators of both environmental impact and industrial processes.<n>We introduce CarboFormer, a lightweight semantic segmentation framework for Optical Gas Imaging (OGI)<n>Our approach integrates an optimized encoder-decoder architecture with specialized multi-scale feature fusion and auxiliary supervision strategies.
  arXiv  Detail & Related papers  (2025-05-23T18:01:42Z)
• Prompt engineering and its implications on the energy consumption of Large Language Models [4.791072577881446]
  Large language models (LLMs) in software engineering pose severe challenges regarding computational resources, data centers, and carbon emissions.<n>In this paper, we investigate how prompt engineering techniques (PETs) can impact the carbon emission of the Llama 3 model for the code generation task.
  arXiv  Detail & Related papers  (2025-01-10T11:49:31Z)
• Generative AI for Low-Carbon Artificial Intelligence of Things with Large Language Models [67.0243099823109]
  Generative AI (GAI) holds immense potential to reduce carbon emissions of Artificial Intelligence of Things (AIoT) In this article, we explore the potential of GAI for carbon emissions reduction and propose a novel GAI-enabled solution for low-carbon AIoT. We propose a Large Language Model (LLM)-enabled carbon emission optimization framework, in which we design pluggable LLM and Retrieval Augmented Generation (RAG) modules.
  arXiv  Detail & Related papers  (2024-04-28T05:46:28Z)
• Towards Sustainable SecureML: Quantifying Carbon Footprint of Adversarial Machine Learning [0.0]
  We pioneer the first investigation into adversarial ML's carbon footprint. We introduce the Robustness Carbon Trade-off Index (RCTI) This novel metric, inspired by economic elasticity principles, captures the sensitivity of carbon emissions to changes in adversarial robustness.
  arXiv  Detail & Related papers  (2024-03-27T21:02:15Z)
• A Comparative Study of Machine Learning Algorithms for Anomaly Detection in Industrial Environments: Performance and Environmental Impact [62.997667081978825]
  This study seeks to address the demands of high-performance machine learning models with environmental sustainability. Traditional machine learning algorithms, such as Decision Trees and Random Forests, demonstrate robust efficiency and performance. However, superior outcomes were obtained with optimised configurations, albeit with a commensurate increase in resource consumption.
  arXiv  Detail & Related papers  (2023-07-01T15:18:00Z)
• Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning [77.62876532784759]
  Machine learning (ML) requires using energy to carry out computations during the model training process. The generation of this energy comes with an environmental cost in terms of greenhouse gas emissions, depending on quantity used and the energy source. We present a survey of the carbon emissions of 95 ML models across time and different tasks in natural language processing and computer vision.
  arXiv  Detail & Related papers  (2023-02-16T18:35:00Z)
• Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI [47.130004596434816]
  In eco2AI we put emphasis on accuracy of energy consumption tracking and correct regional CO2 emissions accounting. The motivation also comes from the concept of AI-based green house gases sequestrating cycle with both Sustainable AI and Green AI pathways.
  arXiv  Detail & Related papers  (2022-07-31T09:34:53Z)
• Measuring the Carbon Intensity of AI in Cloud Instances [91.28501520271972]
  We provide a framework for measuring software carbon intensity, and propose to measure operational carbon emissions. We evaluate a suite of approaches for reducing emissions on the Microsoft Azure cloud compute platform.
  arXiv  Detail & Related papers  (2022-06-10T17:04:04Z)
• Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning [68.37641996188133]
  We introduce a framework for tracking realtime energy consumption and carbon emissions. We create a leaderboard for energy efficient reinforcement learning algorithms. We propose strategies for mitigation of carbon emissions and reduction of energy consumption.
  arXiv  Detail & Related papers  (2020-01-31T05:12:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.