Intrusion Detection System for IoT: Analysis of PSD Robustness

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.