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

2023/09/08 by Tarun Rao Keshabhoina, Keshabhoina, Tarun Rao, Marcos M. Vasconcelos +1
Engineering · #Wireless Communication Security Techniques #IoT Networks and Protocols #Wireless Body Area Networks

paper · pdf · doi:10.48550/arxiv.2309.04088

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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