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

Alberto Ramos (), Honorio Martín, Carmen Cámara and Pedro Peris-Lopez
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Alberto Ramos: Electronic Technology Department, University Carlos III of Madrid, 28911 Leganés, Spain
Honorio Martín: Electronic Technology Department, University Carlos III of Madrid, 28911 Leganés, Spain
Carmen Cámara: Computer Science and Engineering Department, University Carlos III of Madrid, 28911 Leganés, Spain
Pedro Peris-Lopez: Computer Science and Engineering Department, University Carlos III of Madrid, 28911 Leganés, Spain

Data, 2024, vol. 9, issue 5, 1-16

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.

Keywords: on-chip; sensors; identification; microcontrollers; machine learning; deep learning; hardware security; IoT; fingerprinting; PUF (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
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