Green Cybersecurity Metrics for Intrusion Detection Systems

Abstract

Intrusion Detection System (IDS) is a vital solution in the cyber defense architecture, and its implementation should guarantee secure data transactions. This role is becoming increasingly critical as ever-increasing technological democratization has transformed the digital field into a high-risk environment for cyberattacks, leading to an increasing volume of network traffic. Under these circumstances, IDSs will be in higher demand and consume more energy. To address this challenge, the objective of this study is to introduce new sustainability metrics, including Green Feature Extraction (GFE), Redundancy Efficient Rule (RER), Waste Rate (WR), and Path Selection Energy Consumption (PSEC), to quantify the environmental impact of IDS operations. The core of our study examines data flows, algorithms, deployment architecture, configuration, and feature engineering techniques to assess their impact on IDS sustainability. The experimental validation confirms the feasibility and effectiveness of the proposed metrics: the GFE analysis shows that using only 5 features achieves 99.58% detection accuracy with optimal energy efficiency, the WR analysis reveals that Random Forest achieves the lowest waste (0.72% vs. 15.48% for Logistic Regression), and the PSEC analysis demonstrates that Gain Ratio consumes 69% less energy than Chi-Square (29.5 kW vs. 95.4 kW). These findings demonstrate substantial potential for sustainable IDS design that balances robust threat detection with environmental responsibility.