Stimulated Microcontroller Dataset for New IoT Device Identification Schemes through On-Chip Sensor Monitoring Articles uri icon

published in

publication date

  • April 2024

start page

  • 1

end page

  • 16

issue

  • 5

volume

  • 9

International Standard Serial Number (ISSN)

  • 2306-5729

abstract

  • Legitimate identification of devices is crucial to ensure the security of present and future
    IoT ecosystems. In this regard, AI-based systems that exploit intrinsic hardware variations have
    gained notable relevance. Within this context, on-chip sensors included for monitoring purposes
    in a wide range of SoCs remain almost unexplored, despite their potential as a valuable source of
    both information and variability. In this work, we introduce and release a dataset comprising data
    collected from the on-chip temperature and voltage sensors of 20 microcontroller-based boards from
    the STM32L family. These boards were stimulated with five different algorithms, as workloads to
    elicit diverse responses. The dataset consists of five acquisitions (1.3 billion readouts) that are spaced over time and were obtained under different configurations using an automated platform. The raw dataset is publicly available, along with metadata and scripts developed to generate pre-processed T–V sequence sets. Finally, a proof of concept consisting of training a simple model is presented to demonstrate the feasibility of the identification system based on these data.

subjects

  • Computer Science

keywords

  • on-chip; sensors; identification; microcontrollers; machine learning; deep learning; hardware security; iot; fingerprinting; puf