Rigorous Analysis of Data Orthogonalization for Self-Organizing Maps in Machine Learning Cyber Intrusion Detection for LoRa Sensors

In this paper, a novel unsupervised machine learning (ML) algorithm is presented for the expeditious radio frequency (RF) fingerprinting of LoRa modulated chirps. Identification based on received signal strength indicator (RSSI) alone is unlikely to yield a robust means of authentication for critical infrastructure deployment. This is especially true for LoRa where modulated chirps have constant envelope power as well as correlated in-phase/quadrature (I/Q) samples when the chirps are directly extracted. This makes traditional cyber intrusion detection techniques via a convolutional neural network (CNN) impractical. Moreover, we also prove that such correlation leads to an orthogonally inseparable data set, due to which classification becomes intractable. Therefore, we propose an efficient way to produce self-organizing maps (SOMs) of LoRa transmitters (TXs) and a potential rogue node prior to CNN classification. This approach offers SOM orthogonalization, thus minimizing the mean squared error (MSE) within the CNN using our specially constituted SOM engine for precisely profiling each LoRa TX. This method demonstrates cent-percent success in recognizing each LoRa TX as either being a legitimate device or a rogue.