Radio Fingerprinting of Wi-Fi Devices Through MIMO Compressed Channel Feedback

In this paper, we present DeepCSIv2, a data-driven radio fingerprinting (RFP) algorithm to characterize Wi-Fi devices acting as stations (STAs) at the physical layer. Our approach relies on STA-specific hardware impairments extracted from the multiple-input, multiple-output (MIMO) channel state information (CSI) feedback transmitted unencrypted by the STAs to Wi-Fi access points (APs) to establish MIMO transmissions. Recent work showed that such feedback can be effectively used to fingerprint devices acting as APs. In this work, we demonstrate that the same control information can be leveraged to obtain relevant features describing the STAs. Our intuition is that even tiny STA-specific hardware characteristics introduce detectable impairments in the channel estimated by the STA, and percolate in the CSI feedback – consisting of the compressed and quantized channel estimate. DeepCSIv2 is based on a neural network architecture that automatically extracts the STA’s radio fingerprint from the feedback captured over the air and identifies the device. We evaluated DeepCSIv2 through an extensive data collection campaign using 18 commercial IEEE 802.11ax network interface cards (NICs) of the same type from the same vendor. We considered different experiment configurations, changing the propagation environment and the operational bandwidth. The results show that DeepCSIv2 reaches an accuracy higher than 96% in identifying the 18 NICs in our dataset. We will share the dataset and RFP code with the community for reproducibility.