Template attacks are a special kind of sidechannel attacks that work in two stages. In a first stage, the attacker builds up a database of template traces collected from a device which is identical to the attacked device, but under the attacker's control. In the second stage, traces from the target device are compared to the template traces to recover the secret key. In the context of attacking elliptic curve scalar multiplication with template attacks, one can interleave template generation and template matching and reduce the amount of template traces. This paper enhances the power of this technique by defining and applying the concept of online template attacks, a general attack technique with minimal assumptions for an attacker, who has very very limited control over the template device. We show that online template attacks need only one power consumption trace of a scalar multiplication on the target device; they are thus suitable not only against ECDSA and static elliptic curve Diffie-Hellman (ECDH), but also against elliptic curve scalar multiplication in ephemeral ECDH. In addition, online template attacks need only one template trace per scalar bit and they can be applied to a broad variety of scalar multiplication algorithms. To demonstrate the power of online template attacks, we recover scalar bits of a scalar multiplication using the double-and-add-always algorithm on a twisted Edwards curve running on a smartcard with an ATmega163 CPU.

The adoption of deep neural networks for profiled side-channel attacks provides powerful options for leakage detection and key retrieval of secure products. When training a neural network for side-channel analysis, it is expected that the trained model can implement an approximation function that can detect leaking side-channel samples and, at the same time, be insensible to noisy (or non-leaking) samples. This outlines a generalization situation where the model can identify the main representations learned from the training set in a separate test set.This paper discusses how output class probabilities represent a strong metric when conducting the side-channel analysis. Further, we observe that these output probabilities are sensitive to small changes, like selecting specific test traces or weight initialization for a neural network. Next, we discuss the hyperparameter tuning, where one commonly uses only a single out of dozens of trained models, where each of those models will result in different output probabilities. We show how ensembles of machine learning models based on averaged class probabilities can improve generalization. Our results emphasize that ensembles increase a profiled side-channel attack’s performance and reduce the variance of results stemming from different hyperparameters, regardless of the selected dataset or leakage model.

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Finding balanced S-boxes with high nonlinearity and low transparency order is a difficult problem. The property of transparency order is important since it specifies the resilience of an S-box against differential power analysis. Better values for transparency order and hence improved sidechannel security often imply less in terms of nonlinearity. Therefore, it is impossible to find an S-box with all optimal values. Currently, there are no algebraic procedures that can give the preferred and complete set of properties for an S-box. In this paper, we employ evolutionary algorithms to find S-boxes with desired cryptographic properties. Specifically, we conduct experiments for the 8×8 S-box case as used in the AES standard. The results of our experiments proved the feasibility of finding S-boxes with the desired properties in the case of AES. In addition, we show preliminary results of side-channel experiments on different versions of "improved" S-boxes.