# RFID Authentication System Based on User Biometric Information +
## 5 Extension of Authentication System
### Experiment and Evaluation  
In the previous section, we describe the user's behavioral characteristics in detail and introduce how to extract it. We also improve the anomaly detection algorithm and obtain the precise time sequence of user's touches. In this section, we conduct specific experiments to verify the performance of the proposed system. _Experimental setups._ The hardware part of the system consists of an RFID commercial reader, a directional antenna and nine passive tags. The RFID reader is an ImpinjR420 commercial reader, the antenna model is Laird S9028 PCR and the tag model is Alien 9629. We implemented the user interface and verification module on a Thinkpad laptop which collects data from the RFID reader through a low-level reader protocal-LLRP. The final data are processed and analyzed by a computer and MATLAB software. _Metric._ We evaluate the impact of different lengths of the tag sequence that need to be touched on the performance of the scheme, and the lengths are set to 2, 3 and 4, respectively. In addition, with the sliding window's size is set to 0.3, we evaluate the impact of moving step size of the window on the performance. Different lengths will lead to changes in the amount of physiological and behavioral information of users. The longer the tag sequence, the more information about the user's physiological and behavioral characteristics. As shown in Figure 11, when the length is 2, the recognition rate of the system for users is 90.97%. When the length is 3, this figure increases to 97.92%. When the length is 4, this figure increases to 100%. The results show that the longer the sequence is, the more characteristic information is available, resulting in higher accuracy of user authentication. _Impact of moving step size of the sliding window._ In the anomaly detection phase, a different moving step size of the sliding window will lead to a different time accuracy of user's touch obtained by the algorithm, and different time series correspond to different behavioral characteristics of the user. Figure 12 shows the impact of different moving step size of the sliding windows on the performance of user authentication when only behavioral characteristics are used. The results show that the smaller the step size, the higher the accuracy of user authentication. A larger step size leads to poorer performance of authentication. Figure 11: The effect of the length of the specified tag sequence on performance. _Impact of the classification model._ We evaluated six different classification models. To ensure fairness, we used the network structure and used the default parameter settings used in [31]. As can be seen in Figure 13, Random Forest gives the highest relative accuracy (0.959) for our RF-Ubia, and its latency is small. The rest of the classification models were relatively poor. _Time Overhead Evaluation._ When the user touches tags for authentication, the time overhead of the system to process the tag signal data can be divided into three parts, including data preprocessing, anomaly detection and feature extraction. When the length of the tag sequence that users need to touch is 4, it takes 0.376 s on average and 0.52 s at most to process a single data sample, and 0.52 s at most, which can satisfy people's daily authentication needs. The time cost of our method could be lower if a computer with better hardware equipment is available or the data processing algorithm can be further improved. _Comparison with State-of-the-art Authentication System._ To be fair, we compared the Hu-Fu authentication scheme with other authentication systems in the same experimental environment. As shown in Table 2, in the Hu-Fu [8] authentication scheme, if the authentication tag is stolen by an attacker, the attacker can pass authentication without hindrance; in the RF-Mehndi [10] scheme, although the personal card is also resistant to counterfeiting by an attacker if it is lost or stolen, the need to recreate the user card and the collection of information can still cause problems for the user.