基于Kronecker型替换的黑盒多项式稀疏插值

本文基于新的Kronecker型替换,给出两个由黑盒表示的稀疏多项式的新确定性插值算法.令f∈R[x1,……,xn]是一个稀疏黑盒多项式,其次数上界为D.当R是C或者是有限域时,相对于已有算法,新算法具有更好的计算复杂度或者关于D的复杂度更低.特别地,对于一般黑盒模型,D是复杂度中的主要因素,而在所有的确定性算法中,本文的第二个算法的复杂度关于D是最低的.     

Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models

This paper proposes a new method that extends the efficient global optimization to address stochastic black-box systems. The method is based on a kriging meta-model that provides a global prediction of the objective values and a measure of prediction uncertainty at every point. The criterion for the infill sample selection is an augmented expected improvement function with desirable properties for stochastic responses. The method is empirically compared with the revised simplex search, the simultaneous perturbation stochastic approximation, and the DIRECT methods using six test problems from the literature. An application case study on an inventory system is also documented. The results suggest that the proposed method has excellent consistency and efficiency in finding global optimal solutions, and is particularly useful for expensive systems.     

Quantum Attacks on Sum of Even–Mansour Construction with Linear Key Schedules

Shinagawa and Iwata are considered quantum security for the sum of Even–Mansour (SoEM) construction and provided quantum key recovery attacks by Simon’s algorithm and Grover’s algorithm. Furthermore, quantum key recovery attacks are also presented for natural generalizations of SoEM. For some variants of SoEM, they found that their quantum attacks are not obvious and left it as an open problem to discuss the security of such constructions. This paper focuses on this open problem and presents a positive response. We provide quantum key recovery attacks against such constructions by quantum algorithms. For natural generalizations of SoEM with linear key schedules, we also present similar quantum key recovery attacks by quantum algorithms (Simon’s algorithm, Grover’s algorithm, and Grover-meet-Simon algorithm).     

On the robustness of Harris detector in image watermarking attacks

This paper investigates the misplacement issue presented on the detection of feature points by using the well-known Harris detector, under several watermarking attacks. Harris detector, as a powerful tool in finding image's points of high importance, called “interest points”, has been widely used to locate specific image portions where watermarks are embedded, according to some watermarking insertion procedure. Although Harris detector constitutes a significant part of a typical feature-based watermarking process, no work dealing with its performance under certain attacks has been reported yet. This work studies the accuracy of Harris detector in finding the feature points, which define the watermarked regions of attacked images, firstly as an individual processing module and secondly as a part of a feature-based watermarking methodology. Detailed theoretical analysis followed by appropriate simulation experiments has shown that the incorporation of the Harris detector into the watermarking procedure presents misplacement issues that can lead the watermarking algorithm to unreliable results. In the way of decreasing these misplacement errors, a novel algorithm that extracts more stable interest points, with promising performance is also proposed.     

基于主成分-集对分析法的联合火力打击陆军弹药消耗预测方案评估模型研究

陆军作为联合火力打击的重要组成部分,具有反应快、火力猛、精度高、机动灵活的特点,是实施联合火力打击不可缺少的重要力量.陆军在联合火力打击中,如何快速准确的从多种弹药预测方案中分析、评估优选出最佳的陆军弹药消耗预测方案,对提高陆军快速反应能力,提升部队战斗力,打赢未来战争至关重要.为此,在分析影响联合火力打击陆军弹药消耗主要因素的基础上,构建了符合联合火力打击陆军弹药消耗特点的评估指标体系,提出了一种基于主成分和集对分析法综合分析的联合火力打击陆军弹药消耗预测方案的评估优选方法,通过方法可求得指标权重,最终评估优选出最佳的联合火力打击陆军弹药消耗预测方案.结合实例分析,验证构建的评估模型能够用于多种联合火力打击陆军弹药消耗预测方案的评估优选,为进一步提高联合火力打击陆军弹药消耗预测方案提供技术指导.     

一种抗高阶旁路攻击的LED密码算法

LED密码算法是2011年提出的超轻量级密码算法,主要是为资源受限下物联网加密应用研发的.轻量级密码算法结构相对简单,更容易被旁路攻击成功.随机掩码是一种有效抗旁路攻击的方法,在深入LED密码算法结构研究的基础上,提出一种全随机掩码的LED密码算法CMLED.论述了CMLED算法的设计方法,从形式化方面给出了抗高阶旁路攻击选择掩码的原则.同时,对全随机掩码的CMLED与原始算法进行了硬件资源占用与加密效率对比,实验表明CMLED仍然可以高效地在智能卡上实现.     

A switching-based Moving Target Defense against sensor attacks in control systems

Moving Target Defense (MTD) prevents adversaries from being able to predict the effect of their attacks by adding uncertainty in the state of a system during runtime. In this paper, we present an MTD algorithm that randomly changes the availability of the sensor data, so that it is difficult for adversaries to tailor stealthy attacks while, at the same time, minimizing the impact of false-data injection attacks. Using tools from the design of state estimators, namely, observers, and switched systems, we formulate an optimization problem to find the probability of the switching signals that increase the visibility of stealthy attacks while decreasing the deviation caused by false data injection attacks. We show that the proposed MTD algorithm can be designed to guarantee the stability of the closed-loop system with desired performance. In addition, we formulate an optimization problem for the design of the parameters so as to minimize the impact of the attacks. The results are illustrated in two case studies, one about a generic linear time-invariant system and another about a vehicular platooning problem.     

Preprocessing Pipelines including Block-Matching Convolutional Neural Network for Image Denoising to Robustify Deep Reidentification against Evasion Attacks

Artificial neural networks have become the go-to solution for computer vision tasks, including problems of the security domain. One such example comes in the form of reidentification, where deep learning can be part of the surveillance pipeline. The use case necessitates considering an adversarial setting—and neural networks have been shown to be vulnerable to a range of attacks. In this paper, the preprocessing defences against adversarial attacks are evaluated, including block-matching convolutional neural network for image denoising used as an adversarial defence. The benefit of using preprocessing defences comes from the fact that it does not require the effort of retraining the classifier, which, in computer vision problems, is a computationally heavy task. The defences are tested in a real-life-like scenario of using a pre-trained, widely available neural network architecture adapted to a specific task with the use of transfer learning. Multiple preprocessing pipelines are tested and the results are promising.     

Tight and Scalable Side-Channel Attack Evaluations through Asymptotically Optimal Massey-like Inequalities on Guessing Entropy

The bounds presented at CHES 2017 based on Massey’s guessing entropy represent the most scalable side-channel security evaluation method to date. In this paper, we present an improvement of this method, by determining the asymptotically optimal Massey-like inequality and then further refining it for finite support distributions. The impact of these results is highlighted for side-channel attack evaluations, demonstrating the improvements over the CHES 2017 bounds.