Energy-hungry cybersecurity models put climate goals at risk

Cybersecurity systems are consuming growing amounts of energy as artificial intelligence becomes central to detecting threats across networks, cloud services, and critical infrastructure. While AI-driven security tools promise faster and more accurate detection of cyberattacks, a new study warns that this progress carries a hidden environmental cost: the carbon footprint of cybersecurity operations is rising, often without measurement, oversight, or accountability.

That challenge is examined in the study Towards Eco-Friendly Cybersecurity: Machine Learning-Based Anomaly Detection with Carbon and Energy Metrics, published in the International Journal of Applied Mathematics proposes a new framework that evaluates cybersecurity systems not only by how well they detect attacks, but also by how much energy they consume and how much carbon they emit.

Cybersecurity’s blind spot: accuracy without accountability

Modern cybersecurity relies heavily on machine learning to detect anomalies in network traffic that may signal intrusions, malware, or data exfiltration. These systems analyze massive volumes of data in real time, flagging patterns that deviate from normal behavior. Over the past decade, the field has focused almost exclusively on improving detection accuracy, reducing false alarms, and responding faster to threats.

The study argues that this narrow focus has created a blind spot. As models become more complex, they require greater computational resources, leading to higher energy consumption during both training and deployment. In data centers and enterprise environments running continuously, even small increases in per-model energy use can translate into significant carbon emissions over time.

Despite growing awareness of green computing in other areas of AI, cybersecurity has largely escaped scrutiny. Detection systems are often evaluated using traditional metrics such as accuracy, precision, recall, and F1-score, while energy use and emissions are treated as external concerns. The authors identify this gap as increasingly problematic as organizations expand AI-based monitoring across networks, endpoints, and cloud infrastructure.

To address this issue, the study introduces a carbon-aware evaluation framework that integrates energy and emissions data directly into model assessment. Rather than treating sustainability as an afterthought, the framework embeds environmental impact into the core performance evaluation of anomaly detection systems.

The research is built around the Carbon-Aware Cybersecurity Traffic Dataset, a novel dataset that combines network traffic features with real-time energy consumption and carbon intensity data. This allows the authors to measure not only how well models detect anomalies, but also the environmental cost of achieving that performance.

Measuring security and sustainability side by side

Using the dataset, the researchers evaluate five widely used machine learning models: Logistic Regression, Random Forest, Support Vector Machine, Isolation Forest, and XGBoost. Each model is tested under identical conditions to ensure fair comparison, with measurements taken during both training and inference phases.

The results reveal a clear trade-off between model complexity and environmental cost. Ensemble models such as Random Forest and kernel-based models like Support Vector Machine deliver strong detection performance but consume substantially more energy. Their training processes are computationally intensive, and their inference stages require sustained processing power, increasing emissions in continuous monitoring scenarios.

Simpler models, particularly Logistic Regression, demonstrate much lower energy consumption while still achieving competitive detection results. While they may not always match the peak accuracy of more complex approaches, their efficiency makes them attractive for large-scale or always-on deployments.

XGBoost emerges as a middle ground. When carefully tuned, it delivers high detection performance with lower energy use than Random Forest or SVM. The study shows that optimization strategies can significantly reduce the environmental footprint of advanced models without severely compromising accuracy.

To formalize this balance, the authors introduce an Eco-Efficiency Index. This metric captures how much detection performance a model delivers per unit of energy consumed. By combining traditional accuracy metrics with energy data, the index allows direct comparison between models on sustainability grounds.

The study found that slightly lower detection performance achieved far higher eco-efficiency because they consumed a fraction of the energy. This challenges the assumption that maximum accuracy should always be the primary objective in cybersecurity system design.

Feature reduction plays a key role in improving sustainability. The study applies Principal Component Analysis to reduce the dimensionality of network traffic features before training models. This approach significantly lowers computational demand, especially for complex models. In one case, a PCA-optimized Random Forest model retains strong detection capability while reducing energy consumption by more than an order of magnitude.

These results demonstrate that sustainability gains are not limited to choosing simpler models. Thoughtful preprocessing and optimization can dramatically reduce energy use across a range of machine learning techniques.

Redefining cybersecurity performance in a carbon-constrained world

Data centers already account for a significant share of global electricity consumption, and AI-driven security tools contribute to that load.

The authors argue that cybersecurity teams must begin treating energy efficiency as a core design constraint, not a secondary consideration. This does not mean sacrificing security, but rather making informed trade-offs that balance protection with environmental responsibility.

For organizations operating at scale, such as cloud providers, telecom operators, and critical infrastructure managers, the cumulative impact of model choice can be substantial. Selecting eco-efficient detection models can reduce operational costs, lower emissions, and align security practices with corporate sustainability goals.

Lastly, current cybersecurity frameworks emphasize resilience, detection speed, and accuracy, but rarely address energy use. The authors suggest that future standards could incorporate sustainability benchmarks, encouraging vendors and organizations to report the environmental footprint of security tools.