One for All, All for One: A Unified Evaluation Framework for Univariate DPA Attacks

Success Rate (SR) is empirically and theoretically a common metric for evaluating the performance of side-channel attacks. Intuitive expressions of success rate are desirable since they reveal and explain the functional dependence on relevant parameters, such as number of measurements and Signal-to-Noise Ratio (SNR), in a straightforward manner. Meanwhile, existing works more or less expose unsolved fundamental problems, such as strong leakage assumption, difficulty in interpretation of principle, inaccurate evaluation, and inconsideration of high-order SR. In this paper, we first provide an intuitive framework that statistical tests embedded in different univariate DPA attacks are unified as analyzing and comparing visualized vectors in a Euclidean space by using different easy-to-understand metrics. Then, we establish a unified framework to abstract and convert the security evaluations to the problem of finding a boundary in the Euclidean space. With expressions of the boundary, judging whether a DPA attack succeeds in sense of $o^{th}$-order becomes fairly efficient and intuitive, and the corresponding SR can be calculated theoretically by integral. Finally, we propose an algorithm that is capable of estimating arbitrary order of SR effectively. Our experimental results verify the theory and highlight the superiority. We believe our research raises many new perspectives for comparing and evaluating side-channel attacks, countermeasures and implementations

How to Launch a Powerful Side-Channel Collision Attack?

Benefiting from its independence of leakage model, side-channel collision attack is one of the most common distinguishers and attracts wide attention. Although several improvements have been given, its performance on attacking a single collision value has not been significantly improved. Its optimization and efficiency is still an open problem. To solve this, we theoretically analyze the quantitative relationship between encryptions and collisions in this paper, and propose an efficient side-channel attack named Collision-Paired Correlation Attack (CPCA) for low noise scenarios to guarantee that the side with fewer samples in a collision to be detected is completely paired. This optimizes the inefficient utilization of collision information in the existing collision attacks. Moreover, to further exploit the collision information, we maximize the collision pairing, and this optimization significantly improves CPCA and extends our CPCA to large noise scenarios. Finally, to reduce computation complexity, we further optimize our CPCA to a CPA-like distinguisher. Our further theoretical study fully illustrates that our CPCA provides the upper security bound of CECA, and experimental results fully show its superiority

Snowball: Another View on Side-Channel Key Recovery Tools

The performance of Side-Channel Attacks (SCAs) decays rapidly when considering more sub-keys, making the full-key recovery a very challenging problem. Limited to independent collision information utilization, collision attacks establish the relationship among sub-keys but do not significantly slow down this trend. To solve it, we first exploit the samples from the previously attacked S-boxes to assist attacks on the targeted S-box under an assumption that similar leakage occurs in program loop or code reuse scenarios. The later considered S-boxes are easier to be recovered since more samples participate in this assist attack, which results in the ``snowball\u27\u27 effect. We name this scheme as Snowball, which significantly slows down the attenuation rate of attack performance. We further introduce confusion coefficient into the collision attack to construct collision confusion coefficient, and deduce its relationship with correlation coefficient. Based on this relationship, we give two optimizations on our Snowball exploiting the ``values\u27\u27 information and ``rankings\u27\u27 information of collision correlation coefficients named Least Deviation from Pearson correlation coefficient (PLD) and Least Deviation from confusion coefficient (CLD). Experiments show that the above optimizations significantly improve the performance of our Snowball

Improving Magnetic Field Response of Eddy Current Magneto-Optical Imaging for Defect Detection in Carbon Fiber Reinforced Polymers

A large number of carbon fiber reinforced polymers have been applied to aircraft and automobiles, and many nondestructive testing methods have been studied to detect their defects. Eddy current magneto-optical imaging nondestructive testing technology has been widely used in the detection of metal materials such as aircraft skin, but it usually requires a large excitation current and, at present, can only detect metal materials with high conductivity. In order to take full advantage of the innate benefits and efficiency of eddy current magneto-optic imaging and enable it to detect defects in carbon fiber reinforced polymers with weak conductivity, it is necessary to improve the magnetic field response of the eddy current magneto-optic imaging system and explore suitable excitation and detection methods. The scanning eddy current magneto-optical imaging nondestructive testing device built in this study has improved the magnetic field response of the system, and the eddy current magneto-optical phase imaging testing method has been proposed to detect the crack defects of carbon fiber reinforced polymers. The effectiveness of the method has been verified by simulation and experiment

Identification of 38 novel loci for systemic lupus erythematosus and genetic heterogeneity between ancestral groups

The presentation of systemic lupus erythematosus has been known to differ by ancestry, but the underlying genetic factors remain unclear. Here, the authors report ancestry-specific susceptibility loci and better risk prediction when using data from matched ancestral groups