Convolutional Neural Networks with Data Augmentation against Jitter-Based Countermeasures.

International audienceIn the context of the security evaluation of cryptographic implementations, profiling attacks (aka Template Attacks) play a fundamental role. Nowadays the most popular Template Attack strategy consists in approximating the information leakages by Gaussian distributions. Nevertheless this approach suffers from the difficulty to deal with both the traces misalignment and the high dimensionality of the data. This forces the attacker to perform critical preprocessing phases, such as the selection of the points of interest and the realignment of measurements. Some software and hardware countermeasures have been conceived exactly to create such a misalignment. In this paper we propose an end-to-end profiling attack strategy based on the Convolutional Neural Networks: this strategy greatly facilitates the attack roadmap, since it does not require a previous trace realignment nor a precise selection of points of interest. To significantly increase the performances of the CNN, we moreover propose to equip it with the data augmentation technique that is classical in other applications of Machine Learning. As a validation, we present several experiments against traces misaligned by different kinds of countermeasures, including the augmentation of the clock jitter effect in a secure hardware implementation over a modern chip. The excellent results achieved in these experiments prove that Convolutional Neural Networks approach combined with data augmentation gives a very efficient alternative to the state-of-the-art profiling attacks

On the Construction of Side-Channel Attack Resilient S-boxes

info:eu-repo/semantics/publishe

On the Performance of Convolutional Neural Networks for Side-Channel Analysis

In this work, we ask a question whether Convolutional Neural Networks are more suitable for side-channel attacks than some other machine learning techniques and if yes, in what situations. Our results point that Convolutional Neural Networks indeed outperform machine learning in several scenarios when considering accuracy. Still, often there is no compelling reason to use such a complex technique. In fact, if comparing techniques without extra steps like preprocessing, we see an obvious advantage for Convolutional Neural Networks when the level of noise is small, and the number of measurements and features is high. The other tested settings show that simpler machine learning techniques, for a significantly lower computational cost, perform similarly or sometimes even better. The experiments with guessing entropy indicate that methods like Random Forest or XGBoost could perform better than Convolutional Neural Networks for the datasets we investigated

When Similarities Among Devices are Taken for Granted: Another Look at Portability

Contains fulltext : 221072.pdf (Publisher’s version ) (Open Access)AFRICACRYPT 2020