# A Novel RFID Authentication Protocol Based on Reconfigurable RRAM PUF
## 5 Experimental Results Analysis  
### RRAM PUF Test Results  
Attackers can use ML algorithms to map out the circuit structure of PUFs by collecting and analyzing large amounts of CRPs. The authors of [16] provide an elaborate analysis  
Figure 4: The flow chart of the authentication protocol. of ML attacks modeling and achieve good accuracies on various strong PUFs. We ran a simulated ML attack on the RRAM PUF. A total number of \(10^{5}\) CRPs are used, with \(8\times 10^{4}\) for training and \(2\times 10^{4}\) for testing. As shown in Figure 5, the prediction accuracy for RRAM PUF is close to the ideal 50% under machine learning and side-channel attack, which means that attackers fail to make an effective prediction. The accuracy depends on the suitability of the model and can be increased with more training data. Other ML models may exist that can achieve higher accuracy when mapping out the structure of PUF in this work. An ideal strong PUF immune to ML attacks has a 50% bit accuracy when modeled and predicted in any way. Under the correlation-based power analysis attack, the number of traces to successfully analyze RRAM PUF is more than \(1\times 10^{7}\), 20 times as many as that of CMOS PUF. Two important indicators to measure the quality of a PUF are uniqueness and reliability. The former is measured by the inter-hamming distance (inter HD) of the PUF, which requires that the response generated by the PUF is sufficiently different for different challenges. If the uniqueness of PUF as an encryption function is insufficient, it proves that the output has a preference for different inputs, and this preference can easily become a breakthrough for attackers. The latter is measured by PUF and intra-hamming distance (intra HD), which requires that the PUF output response to the same challenge at any time is exactly the same even if the environment changes. Generally, in the design of PUF, in order to reduce the complexity of circuit design, it is stipulated that half of the bits in the challenge sequence are 1 and the other half are 0. For the convenience of discussion, the selected sequence assumes that the challenge sequence is 32 bits, of which 16 bits are 1, and the remaining 16 bits are 0. In the case of selecting a specific row, the sum of the currents of the selected components in each column is obtained. Since half of the address sequence is 1 and half is 0, exactly 16 RRAM devices are selected in each column. Since the components in each column are written with random values in advance, theoretically, the sum of their currents is also random. For the convenience of discussion, it may be assumed that the current flowing through the low-resistance component is a large current, and its value unit is 1, and the current flowing through the high-resistance component is a small current, and its value is 0. For each column of the selected 16 components In other words, the number of components with a high resistance state and the number of components with a low resistance state are random, and different challenges will select different 16 devices. Assuming that the resistance values of the selected 16 components are all high, the sum of the currents of the RRAM devices in this column is 0; if the resistance values of the 16 selected components are all low, the sum of the currents is 16. The sum of these large currents is 16. It is not difficult to obtain that when configured as PUF in this way, the column current is in accordance with the normal distribution, and its symmetry axis is 8; that is, for the 16 RRAM devices selected in each column, the average values of the  
Figure 5: The prediction accuracy for RRAM PUF. current and the expectation are equal to 8.