# FeRHA: Fuzzy-Extractor-Based RF and Hardware Fingerprinting Two-Factor Authentication
## 6 Results and Performance Analysis
### FeRHA Implementation  
To validate the effectiveness of FeRHA, we used a CRP dataset collected using the arbiter-PUF, explained in Section 3.1, implemented on a Xilinx ARTIX-7 FPGA. The PUF is used to map 64-bit challenge bit-strings to 64-bit responses. The RFF is picked based on a dataset of Wi-Fi transmissions based on the IEEE 802.11a/g standard. Such a dataset was obtained through the DARPA RFMLS program and published by ORACLE [62]. The data were collected using an experimental setup of a USRP software-defined radio (SDR), wherein the fixed USRP B210  designated as the receiver. The data frames were emitted from bit-similar USRP X310 radios that conformed to the IEEE 802.11a standard. The data frames were generated utilizing the MATLAB WLAN system toolbox and contained random payloads with shared address fields. Subsequently, the data frames were streamed to the designated SDR for over-the-air wireless transmission. The receiver SDR samples the incoming signals at a sampling rate of 5 MS/s and a center frequency of 2.45 GHz for Wi-Fi. This paper investigates the potential use of machine learning techniques as part of a PUF modeling attack against FeRHA. Specifically, we employ CNN and XGBoost as representative models that an adversary might utilize to perform the attack.  
The successful execution of a cyberattack, including impersonation, data forgery, and man-in-the-middle attacks, is dependent upon the exposure of the underlying device secrets. In the specific context of PUFs, this requires the attacker to accurately model the challenge-response mapping, achieved through machine learning (ML) techniques. It is imperative to note that a key advantage of PUF design is its tamper-resistant nature, and the device secrets (i.e., CRPs) are not stored in the memory. Therefore, our investigative analysis has been focused on thwarting modeling attacks. In our initial attempt to model the arbiter-PUF, we employed CNN and XGBoost without the utilization of our approach. We conducted experiments using varying dataset sizes. The results are depicted in Figure 6. XGBoost achieved a prediction accuracy rate of 99% when using 5000 CRPs, as reported in Figure 6b. To further validate our approach, we carried out another set of experiments using CNN. The CNN architecture comprised an input layer with 32 nodes, along with  
Figure 5: A sequence diagram to illustrate the message exchange between \(\delta_{x}\) and \(\delta_{y}\) during the operation phase.  
two hidden layers that contained 64 and 128 neurons, respectively. An output layer was integrated with a sigmoid activation function [63]. The first three layers utilized a rectified linear activation function (Relu) to achieve optimal performance. The _Adam optimizer_[64] was utilized to update the weights, while _binary cross entropy_ served as the loss function with a learning rate of 0.0001. The model was trained for 150 epochs, with a batch size of 64. Our results indicated that the NN successfully modeled the PUF, with a prediction accuracy rate ranging from 92% to 99%, as depicted in Figure 5(a). FeRHA thwarts the threat of ML-based modeling vulnerability, as demonstrated next.