  The first step of a secure communication is authenticating legible users and
detecting the malicious ones. In the last recent years, some promising schemes
proposed using wireless medium network's features, in particular, channel state
information (CSI) as a means for authentication. These schemes mainly compare
user's previous CSI with the new received CSI to determine if the user is in
fact what it is claiming to be. Despite high accuracy, these approaches lack
the stability in authentication when the users rotate in their positions. This
is due to a significant change in CSI when a user rotates which mislead the
authenticator when it compares the new CSI with the previous ones. Our approach
presents a way of extracting features from raw CSI measurements which are
stable towards rotation. We extract these features by the means of a deep
neural network. We also present a scenario in which users can be {efficiently}
authenticated while they are at certain locations in an environment (even if
they rotate); and, they will be rejected if they change their location. Also,
experimental results are presented to show the performance of the proposed
scheme.
