output voltage change after the additional current the bit is unstable

Output voltage change after the additional current

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output voltage change after the additional current, the bit is unstable. Therefore the error correction method is applied to the current mirror PUF, which gives 1.44% bit error rate with 12000 responses. The corrected output from current mirror PUF is shown in Figure 3. B. Implementing Arbiter PUF The Arbiter PUF was originally designed in 0.18um tech- nology, [7], but here we use TSMC 65nm instead. Variation in the propagation delay of two paths is used to generate a one- bit response, shown in Figure 2. The switching component in the circuit introduces two possible combinations of the top and bottom paths. 16 components were used, and a SR-nand latch at the end gives a binary number based on signal arrival time. The Arbiter PUF can be expressed mathematically by mod- elling its output in terms of the linearity of the delay, [8]: t = sgn (Δ) = sgn ( ω T Φ) , (1) where t stands for the output of the PUF: t = 1 when the Arbiter PUF output is 0 and t = 1 when the output is 1, Δ Fig. 2. Time sequence diagram Fig. 3. Corrected output from CM PUF Fig. 4. Arbiter PUF [7] represents the final delay differences between two paths. The vector ω is a parameter representing the delays in the arbiter PUF and Φ represents feature vectors of the applied challenge. C. Implementing Artificial Neural Network There are a number of ML algorithms such as artificial neu- ral networks (ANN), logistic regression, evolution strategies, etc. The details of modelling attacks can be found elsewhere, [9]. ANNs are one of the ML algorithms that are frequently used to solve practical problems, especially for non-linear systems. The general idea is to collect a large number of CRPs from the objectives and then separate them into two groups. 3rd International Verification and Security Workshop (IVSW) 53
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One group is made available for training the neural network and the other group is for testing. The ANN will have one or more hidden layers to best model the function of a PUF. This method has been implemented by using a built-in Matlab function to find the resilience of the PUFs against ML attacks. The ANN can be improved by applying more specific mathematical algorithms such as the modelling of linearity in delay-based PUFs, which makes the ML more efficient. D. Threat Model Adversaries aim to break the security of the system through invasive and non-invasive methods. We assume that the at- tackers are limited to non-invasive measurements with low- financial status. Attackers can apply challenges to the device and then collect the corresponding responses. Thus a model of the device can be learned from these CRPs, as described in the previous section. III. P ROPOSED D ESIGN The characteristics of a PUF are determined by the structure of the design. Multiple PUFs are combined into a new structure to give a better resistance against attacks. The input of the arbiter PUF is replaced by the output response from the current mirror PUFs. The proposed method increases the complexity of the input and output to (16 × 16) 16 possible outcomes. Ad-
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