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.

本文讨论了基于低功率通信网络的大规模分布式多智能体系统中的拒绝服务攻击问题，并利用神经网络，将推测目标信号的带宽和扩频因子的问题转化为分类问题进行高效实现。