On the Use of CSI for the Generation of RF Fingerprints and Secret Keys
This work addresses authentication and secret key generation for wireless security, presenting an incremental improvement by applying unsupervised ML and signal processing methods.
The paper tackles the problem of using channel state information for physical layer security by separating large scale fading for authentication and small scale fading for secret key generation, demonstrating statistical independence of the extracted stochastic CSI vectors.
This paper presents a systematic approach to use channel state information for authentication and secret key distillation for physical layer security (PLS). We use popular machine learning (ML) methods and signal processing-based approaches to disentangle the large scale fading and be used as a source of uniqueness, from the small scale fading, to be treated as a source of shared entropy secret key generation (SKG). The ML-based approaches are completely unsupervised and hence avoid exhaustive measurement campaigns. We also propose using the Hilbert Schmidt independence criterion (HSIC); our simulation results demonstrate that the extracted stochastic part of the channel state information (CSI) vectors are statistically independent.