RF Fingerprinting of LoRa Transmitters Using Machine Learning with Self-Organizing Maps for Cyber Intrusion Detection

Abstract

In this paper, a novel unsupervised machine learning (ML) algorithm is presented for the expeditious RF fingerprinting of LoRa modulated chirps. Identification based on received signal strength indicator (RSSI) alone is unlikely to yield a robust means for sensor authentication within critical infrastructure deployment. Here, an unsupervised ML algorithm is used to rapidly train an artificial neural network (ANN) matrix creating self-organizing maps (SOMs) for each authentic transmitter and a potential rogue node. A general classifier can be trained on the SOMs for precisely profiling each transmitter as either genuine or rogue. By means of experimental validation, this methodology demonstrated cent-percent success in recognizing each transmitter, either being a real or a rogue node.