Side-channel analysis (SCA) based on machine learning, particularly neural networks, has gained considerable attention in recent years. However, previous works predominantly focus on establishing connections between labels and related profiled traces. These approaches primarily capture label-related features and often overlook the connections between traces of the same label, resulting in the loss of some valuable information. Besides, the attack traces also contain valuable information that can be used in the training process to assist model learning. In this paper, we propose a profiled SCA approach based on contrastive learning named CL-SCA to address these issues. This approach extracts features by emphasizing the similarities among traces, thereby improving the effectiveness of key recovery while maintaining the advantages of the original SCA approach. Through experiments of different datasets from different platforms, we demonstrate that CL-SCA significantly outperforms other approaches. Moreover, by incorporating attack traces into the training process using our approach, we can further enhance its performance. This extension can improve the effectiveness of key recovery, which is fully verified through experiments on different datasets.

This study revisits the side‐channel security of the elliptic curve cryptography (ECC) scalar multiplication implemented with Montgomery ladder. Focusing on a specific implementation that does not use the y ‐coordinate for point addition (ECADD) and point doubling (ECDBL), the authors show that Montgomery ladder on Weierstrass curves is vulnerable to a chosen base‐point attack. Unlike the normal implementation with y ‐coordinate, in the scenario of this study, the chosen base‐point strategy will not lead to operations with two same inputs during the ECADD and/or ECDBL. Instead, by choosing a suitable base‐point, one will find that there are operations that share a common operand; while it is not the case if the base‐point is not chosen correctly. This results in the recovery of the secret (fixed) scalar. They also experiment the methods of shared operand detection on a real‐world SoC, where a secp256k1 dedicated Montgomery ladder scalar multiplication with x ‐only coordinate is implemented, to show the efficiency of the scalar recovery attack. Naturally, the attack can be generalised to other Weierstrass curves when they contain special points.

In side-channel testing, the standard timing analysis works when the vendor can provide a measurement to indicate the execution time of cryptographic algorithms. In this paper, we find that there exists timing leakage in power/electromagnetic channels, which is often ignored in traditional timing analysis. Hence a new method of timing analysis is proposed to deal with the case where execution time is not available. Different execution time leads to different execution intervals, affecting the locations of plaintext and ciphertext transmission. Our method detects timing leakage by studying changes in plaintext-ciphertext correlation when traces are aligned forward and backward. Experiments are then carried out on different cryptographic devices. Furthermore, we propose an improved timing analysis framework which gives appropriate methods for different scenarios.

The module-lattice-based digital signature standard, formerly known as CRYSTALS-DILITHIUM, is a lattice-based post-quantum cryptographic scheme. In August 2024, the National Institute of Standards and Technology officially standardized ML-DSA under FIPS 204. ML-DSA generates one valid signature and multiple rejected signatures during a single signing process. Most side-channel attacks targeting ML-DSA have focused solely on the valid signature, while largely neglecting the hints contained in rejected signatures. Building on prior SASCA frameworks originally proposed for ML-DSA, in this paper we present an efficient and fully practical instantiation of a private-key recovery attack on ML-DSA that jointly exploits side-channel leakages from both valid and rejected signatures within a unified factor graph. This concrete instantiation maximizes the information extracted from a single signing attempt and minimizes the number of required traces for full key recovery. We conducted a proof-of-concept experiment with both reference and ASM-optimized implementations on a Cortex-M4 core chip, where the results demonstrate that incorporating rejected signatures reduces the required number of traces by at least 50.0% for full key recovery. Moreover, we show that using only rejected signatures suffices to recover the key with fewer than 30 traces under our setup. Our findings highlight that protecting rejected signatures is crucial, as their leakage provides valuable side-channel information. We strongly recommend implementing countermeasures for rejected signatures during the signing process to mitigate potential threats.

The application of machine learning techniques in side-channel analysis has recently received increased attention. Finding the best hyperparameters to achieve optimal performance for machine learning models in side-channel analysis is still a challenging endeavor. In order to solve the problem, we present an efficient ensemble framework designed to support profiled side-channel analysis for attacking cryptographic devices with countermeasures. Our proposed framework can partially mitigate the impact of traditional countermeasures employed in cryptographic devices. Additionally, we introduce a novel voting method called elite voting, which leverages candidate keys with higher probabilities to recover the secret key and adjusts the voting weights for better candidate keys. Experimental results illustrate that our proposed framework can effectively recover the right key from cryptographic devices with countermeasures through multiple experiments. It enhances the signal-to-noise ratio of traces and successfully recovers the right key across various datasets. Furthermore, when compared to traditional methods, our elite voting method further enhances the performance of ensemble learning by reducing the number of traces needed to recover the secret key. It exhibits superior performance compared to other ensemble methods, as it can reduce the minimum required number of traces significantly.