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arXiv · 2309.04088

Data-driven classification of low-power communication signals by an unauthenticated user using a software-defined radio

Tarun Rao Keshabhoina·Marcos M. Vasconcelos
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Abstract

Many large-scale distributed multi-agent systems exchange information over low-power communication networks. In particular, agents intermittently communicate state and control signals in robotic network applications, often with limited power over an unlicensed spectrum, prone to eavesdropping and denial-of-service attacks. In this paper, we argue that a widely popular low-power communication protocol known as LoRa is vulnerable to denial-of-service attacks by an unauthenticated attacker if it can successfully identify a target signal's bandwidth and spreading factor. Leveraging a structural pattern in the LoRa signal's instantaneous frequency representation, we relate the problem of jointly inferring the two unknown parameters to a classification problem, which can be efficiently implemented using neural networks.

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Tarun Rao Keshabhoina, Marcos M. Vasconcelos. 2023-09-08. Data-driven classification of low-power communication signals by an unauthenticated user using a software-defined radio. https://arxiv.org/abs/2309.04088

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