Deep Neural Networks Meet CSI-Based Authentication
This addresses authentication stability issues in wireless networks for users and systems, though it appears incremental by adapting existing CSI-based methods with deep learning.
The paper tackles the problem of wireless user authentication using channel state information (CSI), which becomes unstable when users rotate, by proposing a deep neural network to extract rotation-stable features from raw CSI measurements. Experimental results show the scheme can authenticate users at specific locations even during rotation while rejecting location changes.
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.