1 research outputs found
Data-driven classification of low-power communication signals by an unauthenticated user using a software-defined radio
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.Comment: Accepted for presentation at Asilomar Conference on Signals, Systems,
and Computers, 202