The use of deep learning-based side-channel analysis is an effective way of performing profiling attacks on power and electromagnetic leakages, even against targets protected with countermeasures. While many research papers have reported successful results, they typically focus on profiling and attacking a single device, assuming that leakages are similar between devices of the same type. However, this assumption is not always realistic due to variations in hardware and measurement setups, creating what is known as the portability problem. Profiling multiple devices has been proposed as a solution, but obtaining access to these devices may pose a challenge for attackers. This paper proposes a new approach to overcome the portability problem by introducing a neural network layer assessment methodology based on the ablation paradigm. This methodology evaluates the sensitivity and resilience of each layer, providing valuable knowledge to create a Multiple Device Model from Single Device (MDMSD). Specifically, it involves ablating a specific neural network section and performing recovery training. As a result, the profiling model, trained initially on a single device, can be generalized to leakage traces measured from various devices. By addressing the portability problem through a single device, practical side-channel attacks could be more accessible and effective for attackers.

The efficiency of the profiling side-channel analysis can be significantly improved with machine learning techniques. Although powerful, a fundamental machine learning limitation of being data-hungry received little attention in the side-channel community. In practice, the maximum number of leakage traces that evaluators/attackers can obtain is constrained by the scheme requirements or the limited accessibility of the target. Even worse, various countermeasures in modern devices increase the conditions on the profiling size to break the target. This work demonstrates a practical approach to dealing with the lack of profiling traces. Instead of learning from a one-hot encoded label, transferring the labels to their distribution can significantly speed up the convergence of guessing entropy. By studying the relationship between all possible key candidates, we propose a new metric, denoted Label Correlation (LC), to evaluate the generalization ability of the profiling model. We validate LC with two common use cases: early stopping and network architecture search, and the results indicate its superior performance.

This paper proposes a robustness evaluation of an RSA cryptosystem against collision attacks and correlation electromagnetic analysis. Our hardware co-processor is based on the Residue Number System (RNS) in order to perform modular operations over large numbers. To increase its robustness against Side-Channel Analysis, we implemented two different countermeasures. The first one spatially permutates the elements of the RNS bases in order to blur electromagnetic emanations. The second countermeasure aims at randomizing RNS bases before each modular exponentiation. To the best knowledge of authors, this is the first paper that explores the robustness of RNS-RSA against EM analyses.

This paper presents a fully numeric amplitude-demodulation based technique to enhance simple electromagnetic analyses. The technique, thanks to the removal of the clock harmonics and some noise sources, allows efficiently disclosing the leaking information. It has been applied to three different modular exponentiation algorithms, mapped onto the same multiplexed architecture. The latter is able to perform the exponentiation with successive modular multiplications using the Montgomery method. Experimental results demonstrate the efficiency of the applied demodulation based technique and also point out the remaining weaknesses of the considered architecture to retrieve secret keys.