The leaked signals, including electromagnetic, power, timing, and temperature generated during the operation of cryptographic devices, contain highly correlated key value information, leading to security vulnerabilities. In practical operations, due to information collection conditions and time limitations, attackers can only obtain limited valid data. At the same time, the attacker’s data environment cannot be ideal, and noise can affect the acquisition of valid information. Therefore, to improve the effectiveness of obtaining key values from side-channel information analysis in cryptographic devices, we proposed a data augmentation method based on cycle-consistent generative adversarial networks named EME-CycleGAN. By using generators and discriminators, new data are generated to expand the original electromagnetic information dataset, aiming for better modeling effects. For evaluating the data augmentation effect on side-channel electromagnetic signals, we employed the Kolmogorov–Smirnov test to characterize the original and generated data, serving as the evaluation standard for our network model and work. We utilize the existing data to model and verify side-channel attacks, evaluating the impact of the generated information on the overall experimental results. The proposed structure consists of three main parts: side-channel information acquisition, data verification analysis, and determination of attack positions. Experimental results demonstrate that effective attacks on encryption algorithms can be achieved under small-sample dataset conditions.

The traces used in side-channel analysis are essential to breaking the key of encryption and the signal quality greatly affects the correct rate of key guessing. Therefore, the preprocessing of side-channel traces plays an important role in side-channel analysis. The process of side-channel leakage signal acquisition is usually affected by internal circuit noise, external environmental noise, and other factors, so the collected signal is often mixed with strong noise. In order to extract the feature information of side-channel signals from very low signal-to-noise ratio traces, a hybrid threshold denoising framework using singular value decomposition is proposed for side-channel analysis preprocessing. This framework is based on singular value decomposition and introduces low-rank matrix approximation theory to improve the rank selection methods of singular value decomposition. This paper combines the hard threshold method of truncated singular value decomposition with the soft threshold method of singular value shrinkage damping and proposes a hybrid threshold denoising framework using singular value decomposition for the data preprocessing step of side-channel analysis as a general preprocessing method for non-profiled side-channel analysis. The data used in the experimental evaluation are from the raw traces of the public database of DPA contest V2 and AES_HD. The success rate curve of non-profiled side-channel analysis further confirms the effectiveness of the proposed framework. Moreover, the signal-to-noise ratio of traces is significantly improved after preprocessing, and the correlation with the correct key is also significantly enhanced. Experimental results on DPA v2 and AES_HD show that the proposed noise reduction framework can be effectively applied to the side-channel analysis preprocessing step, and can successfully improve the signal-to-noise ratio of the traces and the attack efficiency.

The leakage signals, including electromagnetic, energy, time, and temperature, generated during the operation of password devices contain highly correlated key information, which leads to security vulnerabilities. In traditional encryption algorithms, the length of the key greatly affects the upper limit of its security against cracking. Regarding side-channel attacks on long-key algorithms, traditional template attack methods characterize the energy traces using multivariate Gaussian distribution during the template construction phase. The exhaustive key-guessing process is expected to consume a significant amount of time and computational resources. Therefore, to analyze the effectiveness of obtaining key values from the side information of password devices, we propose an innovative attack method based on a divide-and-conquer logical structure, targeting semi-bytes. We construct a collection of key classification submodules with symmetric correlations. By integrating a differential network model for byte-block sets and an end-to-end direct attack method, we form a holistic symmetric decision framework and propose a key classification structure based on deep transfer learning. This structure consists of three main parts: side information data acquisition, analysis of key-value effectiveness, and determination of attack positions. It employs multiple parallel symmetric subnetworks, effectively improving attack efficiency and reducing the key enumeration range. Experimental results show that the optimal attack accuracy of the network model can reach 91%, with an average attack accuracy of 78%. It overcomes overfitting issues under small sample dataset conditions.

Lightweight block ciphers are normally used in low-power resource-constrained environments, while providing reliable and sufficient security. Therefore, it is important to study the security and reliability of lightweight block ciphers. SKINNY is a new lightweight tweakable block cipher. In this paper, we present an efficient attack scheme for SKINNY-64 based on algebraic fault analysis. The optimal fault injection location is given by analyzing the diffusion of a single-bit fault at different locations during the encryption process. At the same time, by combining the algebraic fault analysis method based on S-box decomposition, the master key can be recovered in an average time of 9 s using one fault. To the best of our knowledge, our proposed attack scheme requires fewer faults, is faster to solve, and has a higher success rate than other existing attack methods.

This article is focused on the electromagnetic radiation effects of Silicon Carbide (SiC) Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs) under total ionizing dose (TID) effect. A 650V trench-gate SiC MOSFET was selected as the experimental subject. The variations in the electronic characteristics of SiC MOSFETs were explained from the perspective of crystal defects. Furthermore, an analysis of the trends in the electromagnetic radiation characteristics of the device was conducted based on the changes in the electrical properties of SiC devices. A broad network model for electromagnetic radiation signals from SiC MOSFETs was proposed. This model facilitates the prediction and assessment of the damage extent and operational status of SiC MOSFET power devices through electromagnetic radiation signals. Additionally, a method for evaluating the on-orbit cycle of SiC devices was introduced based on this network model, presenting a new approach for predicting the lifespan and reliability analysis of SiC MOSFETs operating in space.

In recent years, deep learning methods have become prevalent in the field of side-channel analysis (SCA), leading to a decline in research on nonprofiled attacks and their preprocessing techniques. However, while deep-learning-based SCA methods can reduce the requirements for the quality of leakage signals, they do not fully resolve the need for preprocessing in SCA. Additionally, there is a lack of universal, nontrained preprocessing methods for side-channel leakage under various types of interference. In this article, a new method for preprocessing side channel leakage using synchrosqueezing wavelet transform and ridge energy extraction is proposed to deal with complex scenarios. This method involves two steps: first, applying synchrosqueezing theory to concentrate signal energy in the wavelet domain, and second, utilizing a dynamic path optimization algorithm to locate and extract ridge energy for subsequent attacks. Our proposed solution extracts key information features from the traces, effectively addressing trace distortion caused by varying levels of noise and random disturbances. We validate the effectiveness of our analysis and solution through extensive experiments on four public databases and two self-collected datasets containing power and electromagnetic leakage (EM) from both hardware and software encryption implementations. We compare our experimental results from four indicators: 1) success rate; 2) guessing entropy; 3) signal to noise ratio; and 4) normalized interclass varianc. Besides, we demonstrate the advantages of our method by comparing Measurements to Disclosure and on public datasets. Our method demonstrates significant potential for preprocessing traces in nonprofiled SCA.