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Journal Articles Sensors Year : 2023

Intrusion Detection System for IoT: Analysis of PSD Robustness


The security of internet of things (IoT) devices remains a major concern. These devices are very vulnerable because of some of their particularities (limited in both their memory and computing power, and available energy) that make it impossible to implement traditional security mechanisms. Consequently, researchers are looking for new security mechanisms adapted to these devices and the networks of which they are part. One of the most promising new approaches is fingerprinting, which aims to identify a given device by associating it with a unique signature built from its unique intrinsic characteristics, i.e., inherent imperfections, introduced by the manufacturing processes of its hardware. However, according to state-of-the-art studies, the main challenge that fingerprinting faces is the nonrelevance of the fingerprinting features extracted from hardware imperfections. Since these hardware imperfections can reflect on the RF signal for a wireless communicating device, in this study, we aim to investigate whether or not the power spectral density (PSD) of a device’s RF signal could be a relevant feature for its fingerprinting, knowing that a relevant fingerprinting feature should remain stable regardless of the environmental conditions, over time and under influence of any other parameters. Through experiments, we were able to identify limits and possibilities of power spectral density (PSD) as a fingerprinting feature.
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Dates and versions

hal-04154781 , version 1 (06-07-2023)





Lamoussa Sanogo, Eric Alata, Alexandru Takacs, Daniela Dragomirescu. Intrusion Detection System for IoT: Analysis of PSD Robustness. Sensors, 2023, 23 (4), pp.2353. ⟨10.3390/s23042353⟩. ⟨hal-04154781⟩
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