The profiled side-channel analysis represents the most powerful category of side-channel attacks. In this context, the security evaluator (i.e., attacker) gains access to a profiling device to build a precise model which is used to attack another device in the attacking phase. Mostly, it is assumed that the attacker has significant capabilities in the profiling phase, whereas the attacking phase is very restricted. We step away from this assumption and consider an attacker restricted in the profiling phase, while the attacking phase is less limited. We propose the concept of semi-supervised learning for side-channel analysis, where the attacker uses a small number of labeled measurements from the profiling phase as well as the unlabeled measurements from the attacking phase to build a more reliable model. Our results show that the semisupervised concept significantly helps the template attack (TA) and its pooled version (TAp). More specifically, for low noise scenario, the results for machine learning techniques and TA are often improved when only a small number of measurements is available in the profiling phase, while there is no significant difference in scenarios where the supervised set is large enough for reliable classification. For high noise scenario, TAp and multilayer perceptron results are improved for the majority of inspected dataset sizes, while for high noise scenario with added countermeasures, we show a small improvement for TAp, Naive Bayes and multilayer perceptron approaches for most inspected dataset sizes. Current results go in favor of using semi-supervised learning, especially self-training approach, in side-channel attacks.

The modular exponentiation is crucial to the RSA cryptographic protocol, and variants inspired by the Montgomery ladder have been studied to provide more secure algorithms. In this paper, we abstract away the iterative conditional branching used in the Montgomery ladder, and formalize systems of equations necessary to obtain what we call the semi-interleaved and fully-interleaved ladder properties. In particular, we design fault-injection attacks able to obtain bits of the secret against semi-interleaved ladders, including the Montgomery ladder, but not against fully-interleaved ladders that are more secure. We also apply these equations to extend the Montgomery ladder for both the semi-and fully-interleaved cases, thus proposing novel and more secure algorithms to compute the modular exponentiation.

Abstract. In this article we discus a probability problem applied in the code based cryptography. It is related to the shape of the polynomials with exactly t different roots. We will show that the structure is very dense and the probability that this type of polynomials has at least one coefficient equal to zero is extremelly low. We treated this issue in our research of natural countermeasures to a timing attack against the polynomial evaluation.

The segment of post-quantum cryptography rises its importance with increasing improvements in the quantum computing. Cryptographic post-quantum algorithms have been proposed since 1970s. However, side-channel attack vulnerabilities of these algorithms are still in focus of the recent research. In this paper, we present a differential power analysis attack on the McEliece public-key cryptosystem. We demonstrate that a part of a private key, permutation matrix, can be recovered using the power analysis. We attack a software implementation of a secure bit permutation that was proposed by Strenzke et al. at PQCrypto 2008. The cryptosystem is implemented on a 32-bit ARM based microcontroller. We provide details of the attack and results using power consumption measurements of the device. In addition, we outline a novel countermeasure against the introduced attack. The countermeasure uses properties of the linear codes and does not require large amount of random bits which can be profitable for low-cost embedded devices.