Abstract
Recently introduced to the field of side channel analysis, neural networks have showed to be a powerful and relevant alternative to template attacks. However, their applicability is limited to profiled attack context, as supervised training is needed in order to build a relevant generalized model. When profiling on an open device is not possible, and vertical attacks cannot be applied, the only left possible approach is horizontal attacks. While several contributions have been made for tackling horizontal attacks on asymmetric cryptography algorithms implementations such as RSA or elliptic curve cryptography, their performance remains low and their applicability hard in real life scenario with the presence of high noise. Still, another neural network family known as unsupervised learning neural networks exists, which would not require an open device access and. It must be known if these networks unsupervised learning paradigm and their associated topology can be applied to the context of side-channel attacks and if such is the case, whether or not they can provide better results than traditional methods. Thus, In this work, several approaches are considered to improve clustering based horizontal side channel attacks efficiency. A novel methodology based on statistical analysis is also introduced for univariate points of interest selection. Additionally, an alternative metric for quantifying points of interest exploitability in a clustering attack is proposed and compared to commonly used metrics. The proposed methods allow providing significant improvement over state of the art attacks performance and giving a better explainability of obtained results.