# Multi-WiIR: Multi-User Identity Legitimacy Authentication Based on WiFi Device
## 2 Related Work  
The utilization of WiFi devices for sensing applications has a significant historical background. In 2000, [PERSON] et al. [20] introduced the Radar system, pioneering the use of received signal strength (RSSI) for indoor localization, marking the inception of WiFi for sensing purposes. In 2012, [PERSON] et al. [21] achieved motion sensing through the analysis of Doppler frequency shifts. Similarly, in 2012, [PERSON] et al. [22] utilized a commercial network card CSI from commercial NICs for motion sensing, providing a more refined and stable foundation for WiFi-based identity sensing. In 2016, [PERSON] et al. [11] proposed WiFi-ID, pioneering the integration of WiFi sensing into identity recognition for the first time and leveraging CSI data. Since then, WiFi-based identification systems have undergone extensive development, including non-line-of-sight (NLOS) [12; 13], cross-domain [23; 24; 25], and multi-user [26; 27; 28] WiFi identification approaches. However, in the aforementioned identification systems, the classifier's classification categories are predetermined. Consequently, when an unauthorized individual is present, their category does not match any known categories in the classifier. Despite this, the classifier erroneously classifies them as a known legitimate user. This limitation renders the system incapable of accurately determining the legitimacy of an individual's identity, leaving it vulnerable to intrusion by unauthorized individuals.  
Despite the high accuracies achieved by the current identification techniques, the inability to recognize unauthorized individuals significantly restricts the applicability of these systems. In response to the challenge of authenticating personnel identity legitimacy, several researchers have incorporated this functionality into identification systems. [PERSON] et al. [14] developed the WiFiU system, which categorizes training data into baseline personnel and target personnel. Utilizing a support vector machine (SVM) classifier, the system calculates the probability of an unknown gait instance belonging to the target personnel. Instances with probability values exceeding a threshold are considered legitimate personnel. The study in [15] presents the Wii system, which segregates a segment of the training set into legitimate and illegitimate personnel categories. While accomplishing identity recognition, the system constructs Gaussian models for both legitimate and illegitimate personnel to distinguish between them. [PERSON] et al. [16] identified legitimate user identities and authenticated illegitimate users using specialized loss functions. However, these systems require the introduction of illegitimacy personnel data into the training set, posing challenges in realistic scenarios. [PERSON] et al. [29] established a support vector model for each legitimate user and assessed the legitimacy of unknown personnel identities by comparing the distance between unknown personnel samples and legitimate user support vectors. Nevertheless, this approach is only suitable for a small number of users. [PERSON] et al. [27] analyzed multipath WiFi signals to characterize users with separate CSI profiles. By training threat models with individual CSIs, they achieved an FAR of 8.8% and an FRR of 5.2%. However, this method failed in scenarios where users were positioned along an ellipse with identical arrival times. GaitSense [30] clustered legitimate persons using k-nearest neighbors (KNN) and determined the legitimacy of a target user by assessing their proximity to legitimate persons. Wi-Sniffer [31] employed a trained deep learning model in combination with a decision tree for intruder detection, achieving a correct classification rate of 87% and an intruder detection rate of 95%.  
However, these legitimacy authentication methods are only applicable to single users and not to multiple users. In conclusion, the existing legitimacy authentication systems do not meet the needs of real life.