  In a spoofing attack, an attacker impersonates a legitimate user to access or
modify data belonging to the latter. Typical approaches for spoofing detection
in the physical layer declare an attack when a change is observed in certain
channel features, such as the received signal strength (RSS) measured by
spatially distributed receivers. However, since channels change over time, for
example due to user movement, such approaches are impractical. To sidestep this
limitation, this paper proposes a scheme that combines the decisions of a
position-change detector based on a deep neural network to distinguish spoofing
from movement. Building upon community detection on graphs, the sequence of
received frames is partitioned into subsequences to detect concurrent
transmissions from distinct locations. The scheme can be easily deployed in
practice since it just involves collecting a small dataset of measurements at a
few tens of locations that need not even be computed or recorded. The scheme is
evaluated on real data collected for this purpose.

Signal Processing;Artificial Intelligence