Unsupervised Horizontal Attacks against Public-Key Primitives with DCCA

In order to protect against side-channel attacks, masking countermeasure is widely considered. Its application on asymmetric cryptographic algorithms, such as RSA implementations, rendered multiple traces aggregation inefficient and led to the development of single trace horizontal attacks. Among these horizontal attacks proposed in the literature, many are based on the use of clustering techniques or statistical distinguishers to identify operand collisions. These attacks can be difficult to implement in practice, as they often require advanced trace pre-processing, including the selection of points of interest, a step that is particularly complex to perform in a non-profiling context. In recent years, numerous studies have shown the effectiveness of deep learning in security evaluation for conducting side-channel attacks. However, few attentions have been given to its application in asymmetric cryptography and horizontal attack scenarios. Additionally, the majority of deep learning attacks tend to focus on profiling attacks, which involve a supervised learning phase. In this paper, we propose a new non-profiling horizontal attack using an unsupervised deep learning method called Deep Canonical Correlation Analysis. In this approach, we propose to use a siamese neural network to maximize the correlation between pairs of modular operation traces through canonical correlation analysis, projecting them into a highly correlated latent space that is more suitable for identifying operand collisions. Several experimental results, on simulated traces and a protected RSA implementation with up-to-date countermeasures, show how our proposal outperformed state-of-the-art attacks despite being simpler to implement. This suggests that the use of deep learning can be impactful for security evaluators, even in a non-profiling context and in a fully unsupervised way.