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Intrusion Detection System for IoT: Analysis of PSD Robustness.

Lamoussa Sanogo1,2, Eric Alata1,2, Alexandru Takacs1,3

  • 1Laboratoire d'Analyse et d'Architecture des Systèmes du Centre National de la Recherche Scientifique (LAAS-CNRS), 31077 Toulouse, France.

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Investigating Internet of Things (IoT) device security, this study explores using radio frequency (RF) signal power spectral density (PSD) for unique device fingerprinting. Results identify the potential and limitations of PSD as a stable, relevant security feature.

Keywords:
device fingerprintingdevice identificationinternet of things (IoT)power spectral density (PSD)relevant fingerprinting feature

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Area of Science:

  • Cybersecurity
  • Wireless Communication
  • Hardware Security

Background:

  • Internet of Things (IoT) devices present significant security challenges due to resource constraints, hindering traditional security implementations.
  • Device fingerprinting, leveraging unique hardware imperfections for identification, is a promising alternative.
  • Current fingerprinting methods struggle with the relevance and stability of extracted features.

Purpose of the Study:

  • To evaluate the suitability of the power spectral density (PSD) of a device's radio frequency (RF) signal as a robust feature for IoT device fingerprinting.
  • To determine if PSD remains stable across varying environmental conditions, time, and operational influences, a key requirement for a relevant fingerprinting feature.

Main Methods:

  • Experimental analysis of RF signal characteristics from various IoT devices.
  • Measurement and analysis of the power spectral density (PSD) of the RF signals.
  • Assessment of PSD stability under different environmental and temporal conditions.

Main Results:

  • The study identified specific limitations and possibilities of using PSD as an IoT device fingerprinting feature.
  • Experimental findings indicate that PSD can reflect hardware imperfections but its stability is subject to certain conditions.

Conclusions:

  • Power spectral density (PSD) shows potential as a feature for IoT device fingerprinting, offering insights into hardware-based identification.
  • Further research is needed to overcome the identified limitations and enhance the reliability of PSD-based fingerprinting for secure IoT ecosystems.