Fully Autonomous Z-Score-Based TinyML Anomaly Detection on Resource-Constrained MCUs Using Power Side-Channel Data

arXiv cs.LG / 4/13/2026

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Key Points

The paper introduces a fully autonomous TinyML anomaly detection system that performs both training and inference on a low-power MCU using only power side-channel data.
It uses on-device RMS current sampling and a Z-score thresholding approach to detect deviations, aiming for interpretability and low computational overhead.
The implementation runs on an STM32-based platform and is designed for real-time appliance monitoring without cloud support or external computation.
Experiments on a 14-day mini-fridge dataset reported perfect precision and recall (1.00) and very low inference latency (tens of microseconds).
The approach fits within tight embedded memory budgets (~3.3 KB SRAM, ~63 KB Flash), and the authors plan to extend it with additional lightweight models and multi-device learning.

Abstract

This paper presents a fully autonomous Tiny Machine Learning (TinyML) Z-Score-based anomaly detection system deployed on a low-power microcontroller for real-time monitoring of appliance behavior using power side-channel data. Unlike existing Internet of Things (IoT) anomaly detection approaches that rely on offline training or cloud-assisted analytics, the proposed system performs both model training and inference directly on a resource-constrained microcontroller without external computation or connectivity. The system continuously samples current consumption, computes Root Mean Square (RMS) values on-device, and derives statistical parameters during an initial training phase. Anomalies are detected using lightweight Z-Score thresholds, enabling interpretable and computationally efficient inference suitable for embedded deployment. The architecture was implemented on an STM32-based platform and evaluated using a 14-day dataset collected from a household mini-fridge under normal operation and controlled anomaly conditions. Results demonstrate perfect detection performance, with Precision and Recall of 1.00, inference latencies on the order of tens of microseconds, and a total memory footprint of approximately 3.3 KB SRAM and 63 KB Flash. These results confirm that robust and fully autonomous TinyML anomaly detection can be achieved on low-cost microcontrollers. Future work includes extending the framework to incorporate additional lightweight models and multi-device learning scenarios.