  This work considers a line-of-sight underwater acoustic sensor network
(UWASN) consisting of $M$ underwater sensor nodes randomly deployed according
to uniform distribution within a vertical half-disc (the so-called trusted
zone). The sensor nodes report their sensed data to a sink node on water
surface on a shared underwater acoustic (UWA) reporting channel in a
time-division multiple-access (TDMA) fashion, while an active-yet-invisible
adversary (so-called Eve) is present in the close vicinity who aims to inject
malicious data into the system by impersonating some Alice node. To this end,
this work first considers an additive white Gaussian noise (AWGN) UWA channel,
and proposes a novel, multiple-features based, two-step method at the sink node
to thwart the potential impersonation attack by Eve. Specifically, the sink
node exploits the noisy estimates of the distance, the angle of arrival, and
the location of the transmit node as device fingerprints to carry out a number
of binary hypothesis tests (for impersonation detection) as well as a number of
maximum likelihood hypothesis tests (for transmitter identification when no
impersonation is detected). We provide closed-form expressions for the error
probabilities (i.e., the performance) of most of the hypothesis tests. We then
consider the case of a UWA with colored noise and frequency-dependent pathloss,
and derive a maximum-likelihood (ML) distance estimator as well as the
corresponding Cramer-Rao bound (CRB). We then invoke the proposed two-step,
impersonation detection framework by utilizing distance as the sole feature.
Finally, we provide detailed simulation results for both AWGN UWA channel and
the UWA channel with colored noise. Simulation results verify that the proposed
scheme is indeed effective for a UWA channel with colored noise and
frequency-dependent pathloss.
