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

HMGN1 Modulates Nucleosome Occupancy and DNase I Hypersensitivity at the CpG Island Promoters of Embryonic Stem Cells

Chromatin structure plays a key role in regulating gene expression and embryonic differentiation; however, the factors that determine the organization of chromatin around regulatory sites are not fully known. Here we show that HMGN1, a nucleosome-binding protein ubiquitously expressed in vertebrate cells, preferentially binds to CpG island-containing promoters and affects the organization of nucleosomes, DNase I hypersensitivity, and the transcriptional profile of mouse embryonic stem cells and neural progenitors. Loss of HMGN1 alters the organization of an unstable nucleosome at transcription start sites, reduces the number of DNase I-hypersensitive sites genome wide, and decreases the number of nestin-positive neural progenitors in the subventricular zone (SVZ) region of mouse brain. Thus, architectural chromatin-binding proteins affect the transcription profile and chromatin structure during embryonic stem cell differentiation

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

CL-SCA: Leveraging Contrastive Learning for Profiled Side-Channel Analysis

Side-channel analysis based on machine learning, especially neural networks, has gained significant attention in recent years. However, many existing methods still suffer from certain limitations. Despite the inherent capability of neural networks to extract features, there remains a risk of extracting irrelevant information. The heavy reliance on profiled traces makes it challenging to adapt to remote attack scenarios with limited profiled traces. Besides, attack traces also contain critical information that can be used in the training process to assist model learning. In this paper, we propose a side-channel analysis approach based on contrastive learning named CL-SCA to address these issues. We also leverage a stochastic data augmentation technique to assist model to effectively filter out irrelevant information from the profiled traces. Through experiments of different datasets from different platforms, we demonstrate that CL-SCA significantly outperforms various conventional machine learning side-channel analysis techniques. Moreover, by incorporating attack traces into the training process using our approach, known as CL-SCA+, it becomes possible to achieve even greater enhancements. This extension can further improve the effectiveness of key recovery, which is fully verified through experiments on different datasets

Direct Evidence for Octupole Deformation in 146^{146}Ba and the Origin of Large E1E1 Moment Variations in Reflection-Asymmetric Nuclei

Despite the more than one order of magnitude difference between the measured dipole moments in $^{144}$Ba and $^{146}$Ba, the strength of the octupole correlations in $^{146}$Ba are found to be as strong as those in $^{144}$Ba with a similarly large value of $B(E3;3^- \rightarrow 0^+)$ determined as 48($^{+21}_{-29}$) W.u. The new results not only establish unambiguously the presence of a region of octupole deformation centered on these neutron-rich Ba isotopes, but also manifest the dependence of the electric dipole moments on the occupancy of different neutron orbitals in nuclei with enhanced octupole strength, as revealed by fully microscopic calculations.Comment: 6 pages, 5 figures, accepted for publication in Phys. Rev. Let

Theoretical calculation of cesium deposition and co-deposition with electronegative elements on the plasma grid in negative ion sources

We studied the work function of cesium deposition and co-deposition with the electronegative element on the plasma grid (PG) using the first-principles calculations. The impurity particles may exist in the background plasma and vacuum chamber wall, and the work function of the PG will be affected. The results indicate that the minimum work functions of pure cesium deposition on Mo (110), W (110), and Mo (112) are reached at a partial monolayer. They are 1.66 eV (σ = 0.56 θ), 1.69 eV (σ = 0.75 θ), and 1.75 eV (σ = 0.88 θ), respectively. An appropriate co-deposition model consisting of cesium with electronegative elements can further decrease the work function. The coverage of cesium and electronegative elements are both 0.34 θ in all the co-deposition models. The F-Cs co-deposition model where the Cs atom and F atom are aligned along the surface normal obtains the lowest work function. They are 1.31 eV for F-Cs on Mo (110), and 1.23 eV for F-Cs on W (110), respectively. The change in work function is linearly related to the change in dipole moment density with a slope of −167.03 VÅ. For pure cesium deposition, two factors control the change in dipole-moment density, one is the electron transfer between adsorbates and the substrate, and another one is the restructuring of surface atoms. There are two additional factors for the co-deposition model. One is the intrinsic dipole moment of the double layer, the other is the angle between the intrinsic dipole moment and the surface. The latter two factors play important roles in increasing the total dipole moment

Spatio-temporal divergence in the responses of Finland's boreal forests to climate variables

Spring greening in boreal forest ecosystems has been widely linked to increasing temperature, but few studies have attempted to unravel the relative effects of climate variables such as maximum temperature (TMX), minimum temperature (TMN), mean temperature (TMP), precipitation (PRE) and radiation (RAD) on vegetation growth at different stages of growing season. However, clarifying these effects is fundamental to better understand the relationship between vegetation and climate change. This study investigated spatio-temporal divergence in the responses of Finland's boreal forests to climate variables using the plant phenology index (PPI) calculated based on the latest Collection V006 MODIS BRDF-corrected surface reflectance products (MCD43C4) from 2002 to 2018, and identified the dominant climate variables controlling vegetation change during the growing season (May-September) on a monthly basis. Partial least squares (PLS) regression was used to quantify the response of PPI to climate variables and distinguish the separate impacts of different variables. The study results show the dominant effects of temperature on the PPI in May and June, with TMX, TMN and TMP being the most important explanatory variables for the variation of PPI depending on the location, respectively. Meanwhile, drought had an unexpectedly positive impact on vegetation in few areas. More than 50 % of the variation of PPI could be explained by climate variables for 68.5 % of the entire forest area in May and 87.7 % in June, respectively. During July to September, the PPI variance explained by climate and corresponding spatial extent rapidly decreased. Nevertheless, the RAD was found be the most important explanatory variable to July PPI in some areas. In contrast, the PPI in August and September was insensitive to climate in almost all of the regions studied. Our study gives useful insights on quantifying and identifying the relative importance of climate variables to boreal forest, which can be used to predict the possible response of forest under future warming.Peer reviewe