Advances in cryptography have enabled the features of confidentiality, security, and integrity on small embedded devices such as IoT devices. While mathematically strong, the platform on which an algorithm is implemented plays a significant role in the security of the final product. Side-channel attacks exploit the variations in the system’s physical characteristics to obtain information about the sensitive data. In our scenario, a software implementation of a cryptographic algorithm is flashed on devices from different manufactures with the same instruction set configured for identical execution. To analyze the influence of the microarchitecture on side-channel leakage, we acquire thirty-two sets of power traces from four physical devices. While we notice minor differences in the leakage behavior for different physical boards from the same manufacturer, our results confirm that the difference in microarchitecture implementations of the same core will leak different side-channel information. We also show that TVLA leakage prediction should be treated with caution as it is sensitive to both false positives and negatives.

Today, deep neural networks are a common choice for conducting the profiled side-channel analysis. Unfortunately, it is not trivial to find neural network hyperparameters that would result in top-performing attacks. The hyperparameter leading the training process is the number of epochs during which the training happens. If the training is too short, the network does not reach its full capacity, while if the training is too long, the network overfits and cannot generalize to unseen examples. In this paper, we tackle the problem of determining the correct epoch to stop the training in the deep learning-based side-channel analysis. We demonstrate that the amount of information, or, more precisely, mutual information transferred to the output layer, can be measured and used as a reference metric to determine the epoch at which the network offers optimal generalization. To validate the proposed methodology, we provide extensive experimental results.