To solve the problems of the existing trusted network access protocols for Wireless Local Area Network (WLAN) mesh networks, we propose a new trusted network access protocol for WLAN mesh networks, which is abbreviated as WMN-TNAP. This protocol implements mutual user authentication and Platform-Authentication between the supplicant and Mesh Authenticator (MA), and between the supplicant and Authentication Server (AS) of a WLAN mesh network, establishes the key management system for the WLAN mesh network, and effectively prevents the platform configuration information of the supplicant, MA and AS from leaking out. Moreover, this protocol is proved secure based on the extended Strand Space Model (SSM) for trusted network access protocols.

Wi-Fi-based person identification (PI) tasks are performed by analyzing the fluctuating characteristics of the Channel State Information (CSI) data to determine whether the person's identity is legitimate. This technology can be used for intrusion detection and keyless access to restricted areas. However, the related research rarely considers the restricted computing resources and the complexity of real-world environments, resulting in lacking practicality in some scenarios, such as intrusion detection tasks in remote substations without public network coverage. In this paper, we propose a novel neural network model named SimpleViTFi, a lightweight classification model based on Vision Transformer (ViT), which adds a downsampling mechanism, a distinctive patch embedding method and learnable positional embedding to the cropped ViT architecture. We employ the latest IEEE 802.11ac 80MHz CSI dataset provided by [1]. The CSI matrix is abstracted into a special “image” after pre-processing and fed into the trained SimpleViTFi for classification. The experimental results demonstrate that the proposed SimpleViTFi has lower computational resource overhead and better accuracy than traditional classification models, reflecting the robustness on LOS or NLOS CSI data generated by different Tx-Rx devices and acquired by different monitors.