Combined Effect of Thermal Shock and Hot Corrosion on the Failure of Yttria Stabilized Zirconia Thermal Barrier Coatings

The application of thermal barrier coatings (TBCs) always undergo severe environment, concluding hot corrosion, high temperature oxidation and higher stress, which result in the failure of TBCs. The study of failure mechanisms of TBCs under various combined environmental factors, different with single factor, is more significant to further promote the applications of TBCs. In the present work, the combined effect of thermal shock and hot corrosion on the failure of TBCs was highlighted investigated. The 8wt.% yttria stabilized zirconia (8YSZ) thermal barrier coatings on a GH2132 alloy were deposited by air plasma spraying. The results showed that the transformation of ZrO2 from tetragonal phase to monoclinic phase induced internal stress in the topcoat, when combined with the thermal stress, the topcoat even the bondcoat were both cracked. The main failure of TBCs under the combined effect was in the form of penetrating crack to the bondcoat/substrate interface and oxidation at the penetrating crack front

Leakage Detection with Kolmogorov-Smirnov Test

Leakage detection seeking the evidence of sensitive data dependencies in the side-channel traces instead of trying to recover the sensitive data directly under the enormous efforts with numerous leakage models and state-of-the-art distinguishers can provide a fast preliminary security assessment on the cryptographic devices for designers and evaluators. Therefore, it is a popular topic in recent side-channel research of which the Welch\u27s $t$-test-based Test Vector Leakage Assessment (TVLA) methodology is the most widely used one. However, the TVLA is not always the best option under all kinds of conditions (as we can see in the latter section of this paper). Kolmogorov-Smirnov test is a well-known nonparametric method for statistical analysis to determine whether the samples are from the same distribution by analyzing the cumulative distribution. It has been proposed into side-channel analysis as a successful distinguisher. This paper proposes---to our knowledge, for the first time---Kolmogorov-Smirnov test as a new method for leakage detection. Besides, we propose two implementations to speed up the KS leakage detection procedure. Experimental results on simulated leakage with various parameters and the practical traces verify that KS is an effective and robust leakage detection tool and the comprehensive comparison with TVLA shows that KS-based leakage detection can be a right-hand supplement to TVLA when performing the side-channel assessment

Information Entropy Based Leakage Certification

Side-channel attacks and evaluations typically utilize leakage models to extract sensitive information from measurements of cryptographic implementations. Efforts to establish a true leakage model is still an active area of research since Kocher proposed Differential Power Analysis (DPA) in 1999. Leakage certification plays an important role in this aspect to address the following question: how good is my leakage model? . However, existing leakage certification methods still need to tolerate assumption error and estimation error of unknown leakage models. There are many probability density distributions satisfying given moment constraints. As such, finding the most unbiased and most reasonable model still remains an unresolved problem. In this paper, we address a more fundamental question: what\u27s the true leakage model of a chip? . In particular, we propose Maximum Entropy Distribution (MED) to estimate the leakage model as MED is the most unbiased, objective and theoretically the most reasonable probability density distribution conditioned upon the available information. MED can theoretically use information on arbitrary higher-order moments to infinitely approximate the true leakage model. It well compensates the theory vacancy of model profiling and evaluation. Experimental results demonstrate the superiority of our proposed method for approximating the leakage model using MED estimation

Compressive Sensing based Leakage Sampling and Reconstruction: A First Study

An important prerequisite for Side-channel Attack (SCA) is leakage sampling where the side-channel measurements (e.g. power traces) of the cryptographic device are collected for further analysis. However, as the operating frequency of cryptographic devices continues to increase due to advancing technology, leakage sampling will impose higher requirements on the sampling equipment. This paper undertakes the first study to show that effective leakage sampling can be achieved without relying on sophisticated equipments through Compressive Sensing (CS). In particular, CS can obtain low-dimensional samples from high-dimensional power traces by simply projecting the useful information onto the observation matrix. The leakage information can then be reconstructed in a workstation for further analysis. With this approach, the sampling rate to obtain the side-channel measurements is no longer limited by the operating frequency of the cryptographic device and Nyquist sampling theorem. Instead it depends on the sparsity of the leakage signal. Our study reveals that there is large amount of information redundancy in power traces obtained from the leaky device. As such, CS can employ a much lower sampling rate and yet obtain equivalent leakage sampling performance, which significantly lowers the requirement of sampling equipments. The feasibility of our approach is verified theoretically and through experiments

Wear Evaluation on Ni3Al/MnS Composite Related to Metallurgical Processes

Iron alloyed Ni3Al with composition of Ni-18. 8A1-10. 7Fe0. 5Mn-0. 5Ti-0.2B in atom percent (NAC alloy) showed attractive tribological properties under unlubrication condition at room temperature. The alloy was prepared by hot isostatic pressing (HIP) process. The wear properties were associated with its intrinsic deformation mechanism. Unfortunately, the single phase NAC-alloy worked inadequately with its counterpart disk, and also showed a poor machinability. In the present work, NAC-alloy matrix composite with 6% (volume percent) MnS (manganese sulfide )particle addition was studied to improve its wear behaviors and performance on machining. Two metallurgical processes of HIP and vacuum casting were applied to produce the testing materials. Pin-on-disk (POD) measurements were carried out at room temperature. A commercial vermicular graphite cast iron was selected as a reference material. The counterpart disk was made of a grey cast iron as liner material in ship engines. The contact pressures of 2. 83 MPa and 5. 66 MPa were normally applied in the tests. The investigation indicated that MnS particle addition in the NAC-alloy composites functions as an effective solid lubricant, and improved wear properties and machinability of the materials. Obviously, as-cast NAC-alloy with in-situ formed MnS phase was working more effectively with the counterpart, comparing to the HIPed NAC-alloy composite with MnS particles. At the high contact pressure of 5.66 MPa, the specific wear rate of the as-cast NAC-alloy composite was high. The phenomenon of the negative effect is mostly due to the brittle second NiAl phase as evidenced in the microstructure analysis

Profiling Good Leakage Models For Masked Implementations

Leakage model plays a very important role in side channel attacks. An accurate leakage model greatly improves the efficiency of attacks. However, how to profile a good enough leakage model, or how to measure the accuracy of a leakage model, is seldom studied. Durvaux et al. proposed leakage certification tests to profile good enough leakage model for unmasked implementations. However, they left the leakage model profiling for protected implementations as an open problem. To solve this problem, we propose the first practical higher-order leakage model certification tests for masked implementations. First and second order attacks are performed on the simulations of serial and parallel implementations of a first-order fixed masking. A third-order attack is performed on another simulation of a second-order random masked implementation. The experimental results show that our new tests can profile the leakage models accurately

Towards Optimal Pre-processing in Leakage Detection

An attacker or evaluator can detect more information leakages if he improves the Signal-to-Noise Ratio (SNR) of power traces in his tests. For this purpose, pre-processings such as de-noise, distribution-based traces biasing are used. However, the existing traces biasing schemes can\u27t accurately express the characteristics of power traces with high SNR, making them not ideal for leakage detections. Moreover, if the SNR of power traces is very low, it is very difficult to use the existing de-noise schemes and traces biasing schemes to enhance leakage detection. In this paper, a known key based pre-processing tool named Traces Linear Optimal Biasing (TLOB) is proposed, which performs very well even on power traces with very low SNR. It can accurately evaluate the noise of time samples and give reliable traces optimal biasing. Experimental results show that TLOB significantly reduces number of traces used for detection; correlation coefficients in $\rho$-tests using TLOB approach 1.00, thus the confidence of tests is significantly improved. As far as we know, there is no pre-processing tool more efficient than TLOB. TLOB is very simple, and only brings very limited time and memory consumption. We strongly recommend to use it to pre-process traces in side channel evaluations

Towards Easy Key Enumeration

Key enumeration solutions are post-processing schemes for the output sequences of side channel distinguishers, the application of which are prevented by very large key candidate space and computation power requirements. The attacker may spend several days or months to enumerate a huge key space (e.g. $2^{40}$). In this paper, we aim at pre-processing and reducing the key candidate space by deleting impossible key candidates before enumeration. A new distinguisher named Group Collision Attack (GCA) is given. Moreover, we introduce key verification into key recovery and a new divide and conquer strategy named Key Grouping Enumeration (KGE) is proposed. KGE divides the huge key space into several groups and uses GCA to delete impossible key combinations and output possible ones in each group. KGE then recombines the remaining key candidates in each group using verification. The number of remaining key candidates becomes much smaller through these two impossible key candidate deletion steps with a small amount of computation. Thus, the attacker can use KGE as a pre-processing tool of key enumeration and enumerate the key more easily and fast in a much smaller candidate space

Capturing the Phylogeny of Holometabola with Mitochondrial Genome Data and Bayesian Site-Heterogeneous Mixture Models

After decades of debate, a mostly satisfactory resolution of relationships among the 11 recognized holometabolan orders of insects has been reached based on nuclear genes, resolving one of the most substantial branches of the tree-of-life, but the relationships are still not well established with mitochondrial genome data. The main reasons have been the absence of sufficient data in several orders and lack of appropriate phylogenetic methods that avoid the systematic errors from compositional and mutational biases in insect mitochondrial genomes. In this study, we assembled the richest taxon sampling of Holometabola to date (199 species in 11 orders), and analyzed both nucleotide and amino acid data sets using several methods. We find the standard Bayesian inference and maximum-likelihood analyses were strongly affected by systematic biases, but the site-heterogeneous mixture model implemented in PhyloBayes avoided the false grouping of unrelated taxa exhibiting similar base composition and accelerated evolutionary rate. The inclusion of rRNA genes and removal of fast-evolving sites with the observed variability sorting method for identifying sites deviating from the mean rates improved the phylogenetic inferences under a site-heterogeneous model, correctly recovering most deep branches of the Holometabola phylogeny. We suggest that the use of mitochondrial genome data for resolving deep phylogenetic relationships requires an assessment of the potential impact of substitutional saturation and compositional biases through data deletion strategies and by using site-heterogeneous mixture models. Our study suggests a practical approach for how to use densely sampled mitochondrial genome data in phylogenetic analyses

Manifold Learning Towards Masking Implementations: A First Study

Linear dimensionality reduction plays a very important role in side channel attacks, but it is helpless when meeting the non-linear leakage of masking implementations. Increasing the order of masking makes the attack complexity grow exponentially, which makes the research of nonlinear dimensionality reduction very meaningful. However, the related work is seldom studied. A kernel function was firstly introduced into Kernel Discriminant Analysis (KDA) in CARDIS 2016 to realize nonlinear dimensionality reduction. This is a milestone for attacking masked implementations. However, KDA is supervised and noise-sensitive. Moreover, several parameters and a specialized kernel function are needed to be set and customized. Different kernel functions, parameters and the training results, have great influence on the attack efficiency. In this paper, the high dimensional non-linear leakage of masking implementation is considered as high dimensional manifold, and manifold learning is firstly introduced into side channel attacks to realize nonlinear dimensionality reduction. Several classical and practical manifold learning solutions such as ISOMAP, Locally Linear Embedding (LLE) and Laplacian Eigenmaps (LE) are given. The experiments are performed on the simulated unprotected, first-order and second-order masking implementations. Compared with supervised KDA, manifold learning schemes introduced here are unsupervised and fewer parameters need to be set. This makes manifold learning based nonlinear dimensionality reduction very simple and efficient for attacking masked implementations