Machine Learning-based Robust Physical Layer Authentication Using Angle
of Arrival Estimation
Abstract
In this paper, we study the use of the angle of arrival (AoA) as a
feature for performing robust, machine learning (ML)-based physical
layer authentication (PLA). In fact, whereas most previous research on
PLA relies on physical properties such as channel frequency/impulse
response or received signal strength, the use of the AoA in this context
has not yet been studied in depth as a means of providing resistance to
impersonation (spoofing) attacks. In this study, we first prove that an
effective impersonation attack on AoA-based PLA can only succeed under
very stringent conditions on the attacker in terms of location and
hardware capabilities, and thus, the AoA can in many scenarios be used
as a robust feature for PLA. In addition, we exploit machine learning in
our study to perform lightweight, model-free, intelligent PLA. We show
the effectiveness of the proposed AoA-based PLA solutions by testing
them on experimental outdoor massive multiple input multiple output
data.