Stimulated Microcontroller Dataset for New IoT Device Identification Schemes through On-Chip Sensor Monitoring
Articles
Overview
published in
- Data Journal
publication date
- April 2024
start page
- 1
end page
- 16
issue
- 5
volume
- 9
Digital Object Identifier (DOI)
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.
Classification
subjects
- Computer Science
keywords
- on-chip; sensors; identification; microcontrollers; machine learning; deep learning; hardware security; iot; fingerprinting; puf