Spoofing Attack Detection in the Physical Layer with Robustness to User Movement
This addresses spoofing detection for wireless systems where user movement causes false alarms, but it appears incremental as it builds on existing graph-based and neural network methods.
The paper tackles the problem of distinguishing spoofing attacks from user movement in physical-layer detection by proposing a scheme that combines deep neural network-based position-change detection with community detection on graphs, evaluated on real data.
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.