  Location based services are expected to play a major role in future
generation cellular networks, starting from the incoming 5G systems. At the
same time, localization technologies may be severely affected by attackers
capable to deploy low cost fake base stations and use them to alter
localization signals. In this paper, we concretely focus on two classes of
threats: noise-like jammers, whose objective is to reduce the signal-to-noise
ratio, and spoofing/meaconing attacks, whose objective is to inject false or
erroneous information into the receiver. Then, we formulate the detection
problems as binary hypothesis tests and solve them resorting to the generalized
likelihood ratio test design procedure as well as the Latent Variable Models,
which involves the expectation-maximization algorithm to estimate the unknown
data distribution parameters. The proposed techniques can be applied to a large
class of location data regardless the subsumed network architecture. The
performance analysis is conducted over simulated data generated by using
measurement models from the literature and highlights the effectiveness of the
proposed approaches in detecting the aforementioned classes of attacks.
