Data-driven classification of low-power communication signals by an unauthenticated user using a software-defined radio
This addresses security risks in distributed multi-agent systems like robotic networks, which are prone to eavesdropping and attacks.
The paper tackled the vulnerability of LoRa low-power communication to denial-of-service attacks by showing that an unauthenticated attacker can identify a signal's bandwidth and spreading factor, relating this to a classification problem solvable with neural networks.
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.