Security teams spend a lot of time explaining why detection systems need more compute. Cloud bills rise, models retrain more often, and new analytics pipelines get added to existing stacks. Those conversations usually stay focused on coverage and accuracy. A recent study takes a different approach by measuring anomaly detection models alongside their energy use and associated carbon output, treating compute consumption as part of security operations.
Energy use is becoming part of security operations
Machine learning sits at the center of many detection workflows. Security teams rely on models to flag suspicious traffic, identify abnormal behavior, and support triage decisions. These systems run continuously and retrain on a regular basis. Each cycle draws compute resources that show up in cloud usage reports and internal cost reviews.
Energy consumption also ties into infrastructure planning. Teams managing detection platforms need to decide how often models retrain, how much data they process, and where those workloads run. The study treats energy use as a measurable attribute of detection systems rather than an external concern.
What the research set out to measure
The researchers designed the study to evaluate detection models across two dimensions. One dimension focused on standard detection metrics such as precision, recall, and F1 score. The other dimension tracked energy consumption and carbon emissions during both training and inference.
Experiments ran in a controlled Google Colab environment. The researchers used the CodeCarbon tool to estimate power draw and carbon output based on system activity and regional energy data. This setup allowed consistent measurement across models without deep instrumentation.
The study evaluated several models that security teams already recognize from production and research environments:
- Logistic regression
- Random forest
- Support vector machine
- Isolation forest
- XGBoost
These models represent a range of complexity levels and learning approaches commonly found in intrusion detection systems and network monitoring tools.
Adding energy and carbon data to detection results
To connect detection quality with compute use, the researchers introduced a single metric called the Eco Efficiency Index. The index expresses detection performance relative to energy consumption using the F1 score divided by kilowatt hours consumed.
This approach places accuracy and energy use in the same frame. Security teams can see how much compute a model uses to reach a given detection result without relying on separate dashboards or cost reports.
What stood out in the results
The results showed consistent patterns across the tested models. Simpler models consumed very little energy during both training and inference. More complex ensemble models drew higher levels of compute and produced higher measured emissions within the lab environment.
Optimized models and models trained on reduced feature sets showed strong detection scores with lower energy consumption. Feature reduction using principal component analysis shortened training time and reduced power draw without disrupting detection behavior observed in the dataset.
The study also showed that unsupervised approaches like isolation forest consumed minimal energy due to their design. Detection scores varied based on the dataset structure, which reinforced the importance of pairing sustainability metrics with detection metrics when evaluating models.
Feature reduction and optimization in practice
Feature reduction played a central role in the results. Principal component analysis condensed correlated features into a smaller number of components. This reduced the amount of computation required during training and inference.
Security teams already perform feature selection and tuning as part of routine detection engineering. The research shows that these steps also influence energy consumption in measurable ways. Shorter training cycles reduce compute demand and lower associated emissions across repeated retraining schedules.
Optimization through parameter tuning showed similar effects. Adjusting tree depth, learning rates, and model size changed both detection outcomes and energy use during training runs.
What this means for security teams
The findings give security teams another operational signal to consider when evaluating detection systems. Energy use becomes a measurable attribute alongside detection quality, latency, and coverage.
Teams responsible for budgeting and capacity planning can use this type of data to support decisions about model retraining frequency and deployment scope. Detection engineers can factor compute consumption into model selection during pipeline design. Platform owners gain visibility into how detection workloads contribute to infrastructure usage.
The research also aligns with growing internal reporting requirements related to sustainability and resource consumption. Security tooling often runs continuously and at scale. Measuring energy use at the model level supports broader infrastructure accountability without changing detection objectives.
Where this research fits
The study relies on a limited dataset and a controlled lab environment. Energy values measured in Colab remain small in absolute terms. The results offer directional insight into how model design influences compute behavior when detection systems scale across real networks.
The value of the work lies in its method. Measuring detection performance and energy consumption together gives security teams a way to reason about model behavior beyond accuracy metrics alone.