Exploiting Engineered IQ Samples for Physical Layer Authentication

This paper proposes a physical layer-based authentication scheme that exploits multiple features from the RF-front-end for wireless mesh networks. Specifically, we engineer the in-phase and quadrature-phase (IQ) samples of the legitimate nodes by generating specific ranges of carrier frequency offset (CFO), phase offset (PO), and DC offset (DCO). This engineered IQ governs all multiple legitimate node transmissions (to cover the entire ranges of CFO, PO, and DCO) and follows a specific probability mass function (PMF). We then obtain an optimal function based on the MSE criterion that closely fits the engineered IQ data, which serves as a reference for authenticating network nodes. In the authentication phase, the optimal function obtained from the IQ data transmissions of the respective node requesting authentication is compared with the optimal reference function. Successful authentication occurs when the difference between the optimal function and reference optimal function falls within predefined thresholds of absolute difference, MSE, and correlation coefficient parameters. Specifically, a node is deemed legitimate only when all three criteria meet the threshold requirements. The node undergoes a second authentication check if only one or two criteria are met. Otherwise, it is marked as a possible intruder. We generated extensive I and Q datasets following the IEEE 802.11 standard waveform to validate the proposed scheme, and the necessary metrics were evaluated. The results showed that instead of being used individually when the underlying criteria of MSE, correlation coefficient, and absolute difference are used together can guarantee better authentication, detection, and false detection rates. The findings indicate that the proposed approach attains a 100% authentication rate at a 5 × 10-2 threshold MSE, which represents a 20% improvement over the individual use of MSE.