  In a spoofing attack, an attacker impersonates a legitimate user to access or
tamper with data intended for or produced by the legitimate user. In wireless
communication systems, these attacks may be detected by relying on features of
the channel and transmitter radios. In this context, a popular approach is to
exploit the dependence of the received signal strength (RSS) at multiple
receivers or access points with respect to the spatial location of the
transmitter. Existing schemes rely on long-term estimates, which makes it
difficult to distinguish spoofing from movement of a legitimate user. This
limitation is here addressed by means of a deep neural network that implicitly
learns the distribution of pairs of short-term RSS vector estimates. The
adopted network architecture imposes the invariance to permutations of the
input (commutativity) that the decision problem exhibits. The merits of the
proposed algorithm are corroborated on a data set that we collected.
