  Location information will play a very important role in emerging wireless
networks such as Intelligent Transportation Systems, 5G, and the Internet of
Things. However, wrong location information can result in poor network
outcomes. It is therefore critical to verify all location information before
further utilization in any network operation. In recent years, a number of
information-theoretic Location Verification Systems (LVSs) have been formulated
in attempts to optimally verify the location information supplied by network
users. Such LVSs, however, are somewhat limited since they rely on knowledge of
a number of channel parameters for their operation. To overcome such
limitations, in this work we introduce a Machine Learning based LVS (ML-LVS).
This new form of LVS can adapt itself to changing environments without knowing
the channel parameters. Here, for the first time, we use real-world data to
show how our ML-LVS can outperform information-theoretic LVSs. We demonstrate
this improved performance within the context of vehicular networks using
Received Signal Strength (RSS) measurements at multiple verifying base
stations. We also demonstrate the validity of the ML-LVS even in scenarios
where a sophisticated adversary optimizes her attack location.
