Abstract

The digitalization of smart energy storage and conversion systems enhances efficiency and renewable integration but increases vulnerability to cyberattacks. Conventional cybersecurity measures often fall short in these complex cyber-physical environments, and typical data-driven intrusion detection systems (IDS) prioritize accuracy over energy efficiency and system sustainability. This study proposes an energy-aware, data-driven IDS framework that jointly addresses cybersecurity performance, computational efficiency, and operational stability. Using multivariate time-series data from a battery energy storage system, the framework combines feature engineering with energy-efficient machine learning design. A Random Forest–based IDS is developed and evaluated with standard classification metrics and energy-aware indicators, including detection latency and computational overhead. Experimental results demonstrate strong performance, achieving 93% accuracy, 0.87 AUC, and 0.9964 specificity, with low latency suitable for real-time deployment. Metrics such as the Matthews Correlation Coefficient and Cohen’s Kappa confirm reliable and balanced classification. The framework also evaluates the impact of cyberattacks on energy efficiency and system stability, demonstrating that robust cybersecurity can be achieved without high computational or energy costs. This holistic, energy-aware approach supports secure, resilient, and sustainable operation of next-generation smart energy infrastructures

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