PowerPrint: Harnessing Machine Learning for Accurate and Scalable Device Fingerprinting via Power Consumption
Zhangying He, Hossein Sayadi · 2025
This paper examines the application of machine learning techniques for enhanced device fingerprinting based on power consumption. Existing works on device identification in large-scale networks struggle with accurately pinpointing individual machines among many identical devices, often relying on static fingerprinting or substantial human-involved decision-making, which is inefficient for real-time environments. In this work, we propose PowerPrint, a machine learning-based methodology designed to enhance device fingerprinting by analyzing power consumption data. PowerPrint leverages multiclass classification models to first detect two candidate machines, then further narrows down the identification to a single machine. This two-step online inference process, driven by power consumption analysis, enables precise identification within predefined categories, ensuring accurate and efficient device fingerprinting in large-scale networked systems. Experimental results indicate the effectiveness of our novel approach, with ExtraTrees and MLP classifiers achieving F1-scores of 95% and 93%, respectively, marking an improvement of up to 84% compared to baseline models not employing PowerPrint.