# AI Makes Crypto Evolve
## 4 Airc
The correlation is key for ML attacks to learn the pattern. Furthermore, SRAM cells would invert the challenge bit to provide more secure and random states. In another research study reported in [79], responses were generated by the PUF which were then mapped to a polynomial equation for reconstruction. The mapping of CRPs would be random due to these procedures. It can be used to prevent attacks from ML. The error-tolerant variation would be used when performing authentication to increase the stability of the PUF. The result shows that the ML attack only has an accuracy of 60% for the proposed PUF. Time variables were used in [80] for ML resistance. This research presented a dynamic domain design with an efficient algorithm, namely the Optimal Time Delay Algorithmic (OTPA), to balance the ROPUF CRPs (interval CRPs). CRPs generated using this interval algorithm exhibited a high level of PUF performance in terms of uniqueness and consistency. A similar idea was shown in the proposed algorithm [70], which introduced a new unrelenting Coin FlippingPUF (CF-PUF) that significantly improved resistance against ML attacks. The proposed function utilizes a non-explosive physically strong nonlinearity of ring BRs with respect to threshold voltage variations. In [81], multiple PUFs were used to form a more complex PUF with more robustness against ML attacks. A current mirror PUF and an APUF were combined to form a PUF that was unpredictable for ML algorithms. The output of the current mirror PUF would be placed into the APUF as an input. The result from this PUF would have Configurable Tristate PUF (CTPUF) \((1616)^{16}\) possible outcomes, which depend on delays and voltage. This provides more randomness for the PUF circuit. A similar study was reported in [82]. An APUF combined with Ring Oscillators can provide randomness to prevent ML attacks. Responses generated from the APUF would select ring oscillators. Ring oscillators would be compared through frequencies to form final response bits. The result revealed its higher prediction error when using modern ML algorithms as opposed to regular PUF. Furthermore, the authors of [83] proposed a new design for PUF that improved the existing weak PUF to a strong PUF. The goal for the PUF is to have better resistance against ML attacks. It used a 1-bits MPUF design that contained multiple PicoPUFs with one APUF. The output response bit of PicoPUFs would be used as a challenging bit for APUF to increase security. Hamming distance was used again for randomness measurements. Moreover, a strong subthreshold current counter PUF resilient to ML attacks was proposed in [84]. Similarly, the algorithm proposed in [85] used an output voltages subthreshold array to provide randomness. The array here consisted of unit cells with switch transistors in every one of them. These transistors can have a large variation with respect to voltages. Hamming distance was used to analyze the stability of this PUF. The average BER (temporal stability) in the worst case was only 9%. A similar framework was developed in [86]. In the latter research, a Strong PUF was proposed that uses three subthreshold voltage divider arrays to provide randomness that can resist ML attacks. It uses biases from PUF cells for MOS transistors to generate threshold voltages in a variety of ways. It also allows the PUFs to turn off if there is no work for them to save power. Even with 15,000 training data of CPRs, modern ML techniques such as ANNs can only reach 60% of accuracy. Furthermore, it was reported that a Novel PUF that simulates a double-layer RRAM array structure can be used to resist ML attacks [87]. The read instability of RRAM cells is the key to maintaining RRAM PUF's stability. The proposed algorithm used the 1-RESET-multi-SET method for the continuous current distribution of the RRAM array. As a result, it would have stable reads but many states. A high-temperature environment was used during the testing process, and it revealed high stability compared to a one-layered RRAM PUF.