Machine Learning for Non-Intrusive Profiling of IoT Devices in Noisy Environments
Autori: Shervin Shirmohammadi, University of Ottawa, Canada Marco Carratù, University of Salerno, Italy Ruqiang Yan, Xi’an Jiaotong University, China, Rega V., Tari L., Capriglione D., Molinara M., Pace C. D., Marignetti F., Pace C.D.
Abstract
This paper investigates the impact of noise on the performance of a side-channel-based system for classifying operational scenarios in Internet of Things (IoT) devices. The approach discriminates eight usage scenarios executed on a Raspberry Pi by exploiting physical information leaked during operation, supporting non-intrusive behavioral monitoring and constituting a preliminary step toward side-channel-based intrusion detection in realistic deployment conditions.The acquisition architecture relies on two complementary sensing channels: an electromagnetic probe for radiated emissions and a current probe for power consumption. From the acquired signals, representative time- and frequency-domain features are extracted and used to train and evaluate a machine learning-based classifier.Starting from a baseline framework validated under controlled laboratory conditions, this work evaluates how measurement noise affects classification performance. Noise is injected at the raw signal level and controlled via the signal-to-noise ratio (SNR), considering multiple degradation levels to emulate practical acquisition disturbances typically encountered in real environments. Accuracy is assessed for electromagnetic-only, current-only, and combined configurations to compare channel robustness and feature-fusion effectiveness.Results show a progressive degradation of classification accuracy as noise increases, with electromagnetic features more sensitive than current-based measurements. However, combining both sensing modalities consistently improves robustness across all tested conditions. These findings quantify minimum signal-quality requirements and highlight robustness constraints for reliable side-channel-based scenario classification in realistic IoT environments.
BibTeX
@article{shervinshirmohammadi2026machine,
title={Machine Learning for Non-Intrusive Profiling of IoT Devices in Noisy Environments},
author={Shervin Shirmohammadi and University of Ottawa and Canada Marco Carratù and University of Salerno and Italy Ruqiang Yan and Xi’an Jiaotong University and China and Rega V. and Tari L. and Capriglione D. and Molinara M. and Pace C. D. and Marignetti F. and Pace C.D.},
year={2026},
doi={10.1109/AI4IM69129.2026.11558219}
}