# Multi-WiIR: Multi-User Identity Legitimacy Authentication Based on WiFi Device
###### Abstract  
With the proliferation of WiFi devices, WiFi-based identification technology has garnered attention in the security domain and has demonstrated initial success. Nonetheless, when untrained illegitimate users appear, the classifier tends to categorize them as if they were trained users. In response to this issue, researchers have proposed identity legitimacy authentication systems to identify illicit users, albeit only applicable to individual users. In this article, we propose a multi-user legitimacy authentication system based on WiFi, termed Multi-WiIR. Leveraging WiFi signals, the system captures users' walking patterns to ascertain their legitimacy. The core concept entails training a multi-branch deep neural network, designated WiIR-Net, for feature extraction of individual users. Binary classifiers are then applied to each user, and legitimacy is established by comparing the model's output to predefined thresholds, thus facilitating multi-user legitimacy authentication. Moreover, the study experimentally investigated the impact of the number of legitimate individuals on accuracy rates. The results demonstrated that The Multi-WiIR system showed commendable performance with low latency, being capable of conducting legitimacy recognition in scenarios involving up to four users, with an accuracy rate reaching 85.11%. WiFi sensing; channel state information (CSI); identity legitimacy authentication; multi-user recognition; multi-branch deep neural network +
Footnote †: journal:  
unique characteristics. Hence, biometric-based identification technology is not easily forgotten or lost, nor is it easily replicated or stolen. Moreover, it offers the advantage of user portability, enabling its utilization anytime and anywhere. Commonly employed biometric traits include iris patterns, fingerprints, facial features, and body shape. However, visual devices encounter limitations due to their inability to penetrate through walls, resulting in blind spots during facial and body image recognition. Moreover, they pose risks to personal privacy and may lead to severe privacy breaches [3]. Conversely, the utilization of biometric traits such as iris patterns and fingerprints for identification necessitates direct contact between the individual and specialized equipment. Additionally, the deployment of this equipment in advance may be required for certain temporary uses, with high associated costs [4]. In recent years, researchers have discovered that WiFi technology serves not only communication purposes but also as a means of sensing specific information. Through the utilization of received signal strength indicator (RSSI) and channel state information (CSI), various applications including indoor localization [5; 6], fall detection [7; 8], and monitoring physical activity [9; 10] have been successfully implemented. CSI, being a form of fine-grained physical information, holds a notable advantage in its sensitivity to environmental changes, rendering it more effective in perception. Moreover, WiFi devices are cost-effective and do not require users to interact with or wear additional sensing apparatus, thus circumventing the reliance on visual and wearable devices. Additionally, WiFi sensing technology poses fewer intrusions into user privacy and enhances user comfort and security. Finally, researchers have modified the network card to facilitate stable and easy acquisition of CSI data from WiFi. Furthermore, the data we receive comprise continuous signals. Even in instances where CSI is not available or unavailable, the received data will be deemed erroneous and subsequently discarded during the decoding process. Following this, the next CSI segment will be decoded. With the advancement of CSI-based perception technology, some scholars have integrated CSI into identity recognition systems [11; 12; 13] with notable success. However, certain shortcomings exist within current identification systems, notably the incapacity of the classification rules within classifiers to differentiate unknown samples.