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

Spoofing Attack Detection in the Physical Layer with Robustness to User Movement