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Side-channel attacks represent a powerful category of attacks against cryptographic devices. Still, side-channel analysis for lightweight ciphers is much less investigated than for instance for AES. Although intuition may lead to the conclusion that lightweight ciphers are weaker in terms of side-channel resistance, that remains to be confirmed and quantified. In this paper, we consider various side-channel analysis metrics which should provide an insight on the resistance of lightweight ciphers against side-channel attacks. In particular, for the non-profiled scenario we use the theoretical confusion coefficient and empirical optimal distinguisher. Our study considers side-channel attacks on the first, the last, or both rounds simultaneously. Furthermore, we conduct a profiled side-channel analysis using various machine learning attacks to recover 4-bit and 8-bit intermediate states of the cipher. Our results show that the difference between AES and lightweight ciphers is smaller than one would expect, and even find scenarios in which lightweight ciphers may be more resistant. Interestingly, we observe that the studied 4-bit S-boxes have a different side-channel resilience, while the difference in the 8-bit ones is only theoretically present. [ABSTRACT FROM AUTHOR]

Profiled side-channel attacks consist of several steps one needs to take. An important, but sometimes ignored, step is a selection of the points of interest (features) within side-channel measurement traces. A large majority of the related works start the analyses with an assumption that the features are preselected. Contrary to this assumption, here, we concentrate on the feature selection step. We investigate how advanced feature selection techniques stemming from the machine learning domain can be used to improve the attack efficiency. To this end, we provide a systematic evaluation of the methods of interest. The experiments are performed on several real-world data sets containing software and hardware implementations of AES, including the random delay countermeasure. Our results show that wrapper and hybrid feature selection methods perform extremely well over a wide range of test scenarios and a number of features selected. We emphasize L1 regularization (wrapper approach) and linear support vector machine (SVM) with recursive feature elimination used after chi-square filter (Hybrid approach) that performs well in both accuracy and guessing entropy. Finally, we show that the use of appropriate feature selection techniques is more important for an attack on the high-noise data sets, including those with countermeasures, than on the low-noise ones. [ABSTRACT FROM AUTHOR]