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

• URL: http://arxiv.org/abs/2309.04088v1
• Date: Fri, 8 Sep 2023 02:57:38 GMT
• Title: Data-driven classification of low-power communication signals by an unauthenticated user using a software-defined radio
• Authors: Tarun Rao Keshabhoina and Marcos M. Vasconcelos
• Abstract summary: We argue that a widely popular low-power communication protocol known as LoRa is vulnerable to denial-of-service attacks by an unauthenticated attacker. We relate the problem of jointly inferring the two unknown parameters to a classification problem, which can be efficiently implemented using neural networks.
• Score: 2.3931689873603603
• License: http://creativecommons.org/licenses/by/4.0/
• 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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