An effective simulation analysis of transient electromagnetic multiple faults

Embedded encryption devices and smart sensors are vulnerable to physical attacks. Due to the continuous shrinking of chip size, laser injection, particle radiation and electromagnetic transient injection are possible methods that introduce transient multiple faults. In the fault analysis stage, the adversary is unclear about the actual number of faults injected. Typically, the single-nibble fault analysis encounters difficulties. Therefore, in this paper, we propose novel ciphertext-only impossible differentials that can analyze the number of random faults to six nibbles. We use the impossible differentials to exclude the secret key that definitely does not exist, and then gradually obtain the unique secret key through inverse difference equations. Using software simulation, we conducted 32,000 random multiple fault attacks on Midori. The experiments were carried out to verify the theoretical model of multiple fault attacks. We obtain the relationship between fault injection and information content. To reduce the number of fault attacks, we further optimized the fault attack method. The secret key can be obtained at least 11 times. The proposed ciphertext-only impossible differential analysis provides an effective method for random multiple faults analysis, which would be helpful for improving the security of block ciphers

SPA-GPT: General Pulse Tailor for Simple Power Analysis Based on Reinforcement Learning

Power analysis of public-key algorithms is a well-known approach in the community of side-channel analysis. We usually classify operations based on the differences in power traces produced by different basic operations (such as modular exponentiation) to recover secret information like private keys. The more accurate the segmentation of power traces, the higher the efficiency of their classification. There exist two commonly used methods: one is equidistant segmentation, which requires a fixed number of basic operations and similar trace lengths for each type of operation, leading to limited application scenarios; the other is peak-based segmentation, which relies on personal experience to configure parameters, resulting in insufficient flexibility and poor universality. In this paper, we propose an automated power trace segmentation method based on reinforcement learning algorithms, which is applicable to a wide range of common implementation of public-key algorithms. Reinforcement learning is an unsupervised machine learning technique that eliminates the need for manual label collection. For the first time, this technique is introduced into the field of side-channel analysis for power trace processing. By using prioritized experience replay optimized Deep Q-Network algorithm, we reduce the number of parameters required to achieve accurate segmentation of power traces to only one, i.e. the key length. We also employ various techniques to improve the segmentation effectiveness, such as clustering algorithm, enveloped-based feature enhancement and fine-tuning method. We validate the effectiveness of the new method in nine scenarios involving hardware and software implementations of different public-key algorithms executed on diverse platforms such as microcontrollers, SAKURA-G, and smart cards. Specifically, one of these implementations is protected by time randomization countermeasures. Experimental results show that our method has good robustness on the traces with varying segment lengths and differing peak heights. After employ the clustering algorithm, our method achieves an accuracy of over 99.6% in operations recovery. Besides, power traces collected from these devices have been uploaded as databases, which are available for researchers engaged in public-key algorithms to conduct related experiments or verify our method

Allelic shift in cis-elements of the transcription factor RAP2.12 underlies adaptation associated with humidity in Arabidopsis thaliana

Populations of widespread species are usually geographically distributed through contrasting stresses, but underlying genetic mechanisms controlling this adaptation remain largely unknown. Here, we show that in Arabidopsis thaliana, allelic changes in the cis-regulatory elements, WT box and W box, in the promoter of a key transcription factor associated with oxygen sensing, RELATED TO AP 2.12 (RAP2.12), are responsible for differentially regulating tolerance to drought and flooding. These two cis-elements are regulated by different transcription factors that downstream of RAP2.12 results in differential accumulation of hypoxia-responsive transcripts. The evolution from one cis-element haplotype to the other is associated with the colonization of humid environments from arid habitats. This gene thus promotes both drought and flooding adaptation via an adaptive mechanism that diversifies its regulation through noncoding alleles

Analysis of Electromagnetic Information Leakage Based on Cryptographic Integrated Circuits

Cryptographic algorithm is the most commonly used method of information security protection for many devices. The secret key of cryptographic algorithm is usually stored in these devices’ registers. In this paper, we propose an electromagnetic information leakage model to investigate the relationship between the electromagnetic leakage signal and the secret key. The registers are considered as electric dipole models to illustrate the source of the electromagnetic leakage. The equivalent circuit of the magnetic field probe is developed to bridge the output voltage and the electromagnetic leakage signal. Combining them, the electromagnetic information leakage model’s function relationship can be established. Besides, an electromagnetic leakage model based on multiple linear regression is proposed to recover the secret key and the model’s effectiveness is evaluated by guess entropy. Near field tests are conducted in an unshielded ordinary indoor environment to investigate the electromagnetic side-channel information leakage. The experiment result shows the correctness of the proposed electromagnetic leakage model and it can be used to recover the secret key of the cryptographic algorithm

Genome-Wide Analysis of the Homeobox Gene Family and Identification of Drought-Responsive Members in <i>Populus trichocarpa</i>

Homeobox (HB) genes play critical roles in the regulation of plant morphogenesis, growth and development. Here, we identified a total of 156 PtrHB genes from the Populus trichocarpa genome. According to the topologies and taxonomy of the phylogenetic tree constructed by Arabidopsis thaliana HB members, all PtrHB proteins were divided into six subgroups, namely HD-ZIP, ZF-HD, HB-PHD, TALE, WOX and HB-OTHERS. Multiple alignments of conserved homeodomains (HDs) revealed the conserved loci of each subgroup, while gene structure analysis showed similar exon–intron gene structures, and motif analysis indicated the similarity of motif number and pattern in the same subgroup. Promoter analysis indicated that the promoters of PtrHB genes contain a series of cis-acting regulatory elements involved in responding to various abiotic stresses, indicating that PtrHBs had potential functions in these processes. Collinearity analysis revealed that there are 96 pairs of 127 PtrHB genes mainly distributing on Chromosomes 1, 2, and 5. We analyzed the spatio-temporal expression patterns of PtrHB genes, and the virus-induced gene silencing (VIGS) of PtrHB3 gene resulted in the compromised tolerance of poplar seedlings to mannitol treatment. The bioinformatics on PtrHB family and preliminary exploration of drought-responsive genes can provide support for further study of the family in woody plants, especially in drought-related biological processes. It also provides a direction for developing new varieties of poplar with drought resistance. Overall, our results provided significant information for further functional analysis of PtrHB genes in poplar and demonstrated that PtrHB3 is a dominant gene regulating tolerance to water stress treatment in poplar seedlings

A method for process parameter optimization of simultaneous double-sided friction stir welding using a heat transfer model

This paper presents a method for optimizing process parameters of simultaneous double-sided friction stir welding (SDS-FSW). Building upon the thermal pseudo-mechanical mechanism and computational solid mechanics, a heat transfer model is formulated first to investigate the coupling effect of the heat sources and verified by existing experimental data of another study. The nonlinear surrogate models are then constructed to relate three process parameters with maximum temperature at two selected locations and heat-affected zone (HAZ) length, using the data generated by the heat transfer model. Consequently, the spindle speed, feed rate, as well as distance between two welding tools are optimized by minimizing the input energy subject to the constraints in terms of the maximum temperature and HAZ length. Compared to the initial parameters, the optimal case allowed the welding parameters including the HAZ length, input energy, and welding time to reduce by 5.71%, 37.46%, and 20.32%, respectively, thereby having the potential applicability to achieve relatively high welding quality and efficiency

Multivariate Rank-Based Analysis of Multiple Endpoints in Clinical Trials: A Global Test Approach

Clinical trials often involve the assessment of multiple endpoints to comprehensively evaluate the efficacy and safety of interventions. In the work, we consider a global nonparametric testing procedure based on multivariate rank for the analysis of multiple endpoints in clinical trials. Unlike other existing approaches that rely on pairwise comparisons for each individual endpoint, the proposed method directly incorporates the multivariate ranks of the observations. By considering the joint ranking of all endpoints, the proposed approach provides robustness against diverse data distributions and censoring mechanisms commonly encountered in clinical trials. Through extensive simulations, we demonstrate the superior performance of the multivariate rank-based approach in controlling type I error and achieving higher power compared to existing rank-based methods. The simulations illustrate the advantages of leveraging multivariate ranks and highlight the robustness of the approach in various settings. The proposed method offers an effective tool for the analysis of multiple endpoints in clinical trials, enhancing the reliability and efficiency of outcome evaluations

A Novel Multi-Objective Electromagnetic Analysis Based on Genetic Algorithm

Correlation electromagnetic analysis (CEMA) is a method prevalent in side-channel analysis of cryptographic devices. Its success mostly depends on the quality of electromagnetic signals acquired from the devices. In the past, only one byte of the key was analyzed and other bytes were regarded as noise. Apparently, other bytes&rsquo; useful information was wasted, which may increase the difficulty of recovering the key. Multi-objective optimization is a good way to solve the problem of a single byte of the key. In this work, we applied multi-objective optimization to correlation electromagnetic analysis taking all bytes of the key into consideration. Combining the advantages of multi-objective optimization and genetic algorithm, we put forward a novel multi-objective electromagnetic analysis based on a genetic algorithm to take full advantage of information when recovering the key. Experiments with an Advanced Encryption Standard (AES) cryptographic algorithm on a Sakura-G board demonstrate the efficiency of our method in practice. The experimental results show that our method reduces the number of traces required in correlation electromagnetic analysis. It achieved approximately 42.72% improvement for the corresponding case compared with CEMA

Multibyte Electromagnetic Analysis Based on Particle Swarm Optimization Algorithm

This paper focuses on electromagnetic information security in communication systems. Classical correlation electromagnetic analysis (CEMA) is known as a powerful way to recover the cryptographic algorithm&rsquo;s key. In the classical method, only one byte of the key is used while the other bytes are considered as noise, which not only reduces the efficiency but also is a waste of information. In order to take full advantage of useful information, multiple bytes of the key are used. We transform the key into a multidimensional form, and each byte of the key is considered as a dimension. The problem of the right key searching is transformed into the problem of optimizing correlation coefficients of key candidates. The particle swarm optimization (PSO) algorithm is particularly more suited to solve the optimization problems with high dimension and complex structure. In this paper, we applied the PSO algorithm into CEMA to solve multidimensional problems, and we also add a mutation operator to the optimization algorithm to improve the result. Here, we have proposed a multibyte correlation electromagnetic analysis based on particle swarm optimization. We verified our method on a universal test board that is designed for research and development on hardware security. We implemented the Advanced Encryption Standard (AES) cryptographic algorithm on the test board. Experimental results have shown that our method outperforms the classical method; it achieves approximately 13.72% improvement for the corresponding case