1. PN-SCA : a high generalization and fast profiled SCA based on prototypical networksYu Ou, Yongzhuang Wei, Changhai Ou, Enes Pašalić, 2026, original scientific article Abstract: Recently, there has been a growing interest in scenarios that employ a few (imbalanced) power traces for deep learning based side-channel analysis (DL-SCA). Finding a lightweight DL-SCA architecture that is more generalizable and faster to learn is of great importance when handling such situations. In this work, an initial analysis is conducted on the capability of learning and the susceptibility to attacks of prevalent DL-SCA models, focusing on cases that culminate in an unsuccessful attack. Subsequently, a meta-learning technique, known as prototypical networks, is delineated for the construction of a lightweight deep learning framework. In contrast to conventional DL-SCA models, the proposed architecture, designated as PN-SCA, does not predict the probability of a power trace belonging to an intermediate value in classification. Instead, it facilitates the learning of an encoder capable of embedding power consumption data within a latent space, while also establishing templates, or prototypes, for diverse categories. Moreover, we developed a metric that is specifically intended for the selection of hyperparameters due to the unique training phase of PN-SCA. Finally, four distinct scenarios are established with few (including one imbalanced) power traces to evaluate the correctness of our architecture. The results clearly illustrate the advantages of our PN-SCA in terms of generalization, reduced training costs (with a decrease in profiling traces by over 50%), and significantly enhanced attack effect (with a reduction of power traces requirements by over 90%), thus demonstrating notable improvements over existing methodologies
Keywords: side-channel analysis, deep learning based SCA, prototypical networks, few-shot learning
Published in RUP: 20.05.2026; Views: 314; Downloads: 8
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2. A lightweight deep learning model for profiled SCA based on random convolution kernelsYu Ou, Yongzhuang Wei, René Rodríguez, Fengrong Zhang, 2025, original scientific article Abstract: In deep learning-based side-channel analysis (DL-SCA), there may be a proliferation of model parameters as the number of trace power points increases, especially in the case of raw power traces. Determining how to design a lightweight deep learning model that can handle a trace with more power points and has fewer parameters and lower time costs for profiled SCAs appears to be a challenge. In this article, a DL-SCA model is proposed by introducing a non-trained DL technique called random convolutional kernels, which allows us to extract the features of leakage like using a transformer model. The model is then processed by a classifier with an attention mechanism, which finally outputs the probability vector for the candidate keys. Moreover, we analyze the performance and complexity of the random kernels and discuss how they work in theory. On several public AES datasets, the experimental results show that the number of required profiling traces and trainable parameters reduce, respectively, by over 70% and 94% compared with state-of-the-art works, while ensuring that the number of power traces required to recover the real key is acceptable. Importantly, differing from previous SCA models, our architecture eliminates the dependency between the feature length of power traces and the number of trainable parameters, which allows for the architecture to be applied to the case of raw power traces.
Keywords: side-channel analysis, deep learning, convolution neural networks, random convolution kernel
Published in RUP: 26.09.2025; Views: 1983; Downloads: 9
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3. On characterization of transparency order for (n, m)-functionsYu Zhou, Yongzhuang Wei, Hailong Zhang, LuYang Li, Enes Pašalić, Wenling Wu, 2021, published scientific conference contribution Keywords: (n, m)-functions, transparency order, nonlinearity, cross-correlation
Published in RUP: 18.11.2021; Views: 3108; Downloads: 28
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