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Detecting Fault Injection Attacks Based on Compressed Sensing and integer linear programming

IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2019年第3期16卷 476-483页

作者： Li, Huiyun Shao, Cuiping Wang, Zheng Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Univ Chinese Acad Sci Shenzhen Coll Adv Technol Shenzhen 518055 Peoples R China Chinese Univ Hongkong Shenzhen 518055 Peoples R China

Cryptographic ICs have been widely applied to numerous security-critical environments nowadays. Fault injection has become a serious attack on cryptographic IC, especially soft-errors or single event upsets (SEUs) by fine-resolution fault injection attacks. Detection and tamper evidence of these attacks become important. Traditional SEU diagnose methods usually require special sensors embedded into the circuits. However, these methods require non-trivial design and test effort, and usually just yield statistic results. In this paper, we formulate the detection fault injection attacks as a compressed sensing problem, due to sparsity of soft errors. Besides, due to the binary characteristic of the coefficient matrix and the variables, integer linear programming is adopted to reconstruct the soft error signals. Simulation results on a cryptographic IC demonstrate that the proposed method is capable to accurately detect the locations of soft-errors caused by fault injection attacks with negligible hardware overhead. The abnormal test output of scan-chains can be tamper evidence of the fault injection attacks.

关键词： Fault injection attacks compressed sensing integer linear programming soft-errors detection tamper evidence

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Lowest thermal transmittance of an external wall under budget, material and thickness restrictions: An integer linear programming approach

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ENERGY AND BUILDINGS 2018年 158卷 222-233页

作者： Soler, David Salandin, Andrea Mico, Joan C. Univ Politecn Valencia Inst Univ Matemat Multidisciplinar Cami Vera S-N E-46022 Valencia Spain Univ Politecn Valencia Ctr Tecnol Fis Cami Vera S-N E-46022 Valencia Spain

This paper deals with the minimization of a building's external wall thermal transmittance, with the aim of improving the energy efficiency of the building. The wall's thermal transmittance must abide by the current legislation, but also suit the limitations of other construction parameters, mainly budget and thickness, but also time limit, workforce, number and thickness of the layers and availability of materials depending on the approach. The optimization is achieved formulating an integer linear programming (ILP) problem involving the parameters mentioned above. Therefore, any available ILP solver can be run to obtain the best combination of the different materials and thicknesses for the layers, in order to minimize the thermal transmittance. This paper presents a case study of a common but representative external wall consisting of 6 layers, with more than 670,000 possible combinations of materials and their thicknesses. The study concludes with a comparison of the lowest thermal transmittance obtained for a selection of budget and thickness combinations for the mentioned wall. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Thermal transmittance Building envelope External wall integer linear programming Building process Budget

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Static Mapping of Applications on Heterogeneous Multi-Core Platforms Combining Logic-Based Benders Decomposition with integer linear programming

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ACM TRANSACTIONS ON DESIGN AUTOMATION OF ELECTRONIC SYSTEMS 2018年第2期23卷 1–24页

作者： Emeretlis, Andreas Theodoridis, George Alefragis, Panayiotis Voros, Nikolaos Univ Patras VLSI Design Lab Dept Elect & Comp Engn Rion 26504 Greece Technol Educ Inst Western Greece Embedded Syst Design & Applicat Lab Comp & Informat Engn Dept Antirrio 30020 Greece

The proper mapping of an application on a multi-core platform and the scheduling of its tasks are key elements to achieve the maximum performance. In this article, a novel hybrid approach based on integrating the Logic-Based Benders Decomposition (LBBD) principle with a pure integer linear programming (ILP) model is introduced for mapping applications described by Directed Acyclic Graphs (DAGs) on platforms consisting of heterogeneous cores. The LBBD approach combines two optimization techniques with complementary strengths, namely ILP and Constraint programming (CP), and is employed as a cut generation scheme. The generated constraints are utilized by the ILP model to cut possible assignment combinations aiming at improving the solution or proving the optimality of the best-found one. The introduced approach was applied both on synthetic DAGs and on DAGs derived from real applications. Through the proposed approach, many problems were optimally solved that could not be solved by any of the above methods (ILP, LBBD) alone within a time limit of 2 hours, while the overall solution time was also significantly decreased. Specifically, the hybrid method exhibited speedups equal to 4.2 x for the synthetic instances and 10 x for the real-application DAGs over the LBBD approach and two orders of magnitude over the ILP model.

关键词： Task scheduling multi-core embedded platforms integer linear programming constraint programming Benders decomposition

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Edge metric dimensions via hierarchical product and integer linear programming

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OPTIMIZATION LETTERS 2021年第6期15卷 1993-2003页

作者： Klavzar, Sandi Tavakoli, Mostafa Univ Ljubljana Fac Math & Phys Ljubljana Slovenia Univ Maribor Fac Nat Sci & Math Maribor Slovenia Inst Math Phys & Mech Ljubljana Slovenia Ferdowsi Univ Mashhad Fac Math Sci Dept Appl Math POB 1159 Mashhad 91775 Razavi Khorasan Iran

If S = {v(1),..., v(k)} is an ordered subset of vertices of a connected graph G and e is an edge of G, then the vector r(G)(e vertical bar S) = (d(G)(v(1), e),..., d(G)(v(k), e)) is the edge metric S-representation of e. If the vertices of G have pairwise different edge metric S-representations, then S is an edge metric generator for G. The cardinality of a smallest edge metric generator is the edge metric dimension edim(G) of G. A general sharp upper bound on the edge metric dimension of hierarchical products G(U) Pi H is proved. Exact formula is derived for the case when vertical bar U vertical bar = 1. An integer linear programming model for computing the edge metric dimension is proposed. Several examples are provided which demonstrate how these two methods can be applied to obtain the edge metric dimensions of some applicable graphs.

关键词： Metric dimension Edge metric dimension Hierarchical product integer linear programming Molecular graph

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Using integer linear programming for instruction scheduling and register allocation in multi-issue processors

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 1997年第9期34卷 1-14页

作者： Chang, CM Chen, CM King, CT Department of Computer Science National Tsing Hua University Hsinchu Taiwan 300 R.O.C.

Instruction scheduling and register allocation are two very important optimizations in modern compilers for advanced processors. These two optimizations must be performed simultaneously in order to maximize the instruction-level parallelism and to fully utilize the registers [1]. In this paper, we solve register allocation and instruction scheduling simultaneously using integer linear programming (ILP). We have successfully worked out the ILP formulations for the problem with and without register spilling. Two kinds of optimizations are considered: (1) fix the number of free registers and then solve the minimum number of cycles to execute the instructions, or (2) fix the maximum execution cycles for the instructions and solve the minimum number of registers needed. Besides being theoretically interesting, our solution serves as a reference point for other heuristic solutions. The formulations are also applicable to high-level synthesis of ASICs and designs for embedded processors. In these application domains, the code quality is more important than the compilation time.

关键词： integer linear programming compiler optimization instruction scheduling register allocation processor architecture

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Ensemble Segmentation Using Efficient integer linear programming

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2012年第10期34卷 1966-1977页

作者： Alush, Amir Goldberger, Jacob Bar Ilan Univ Fac Engn IL-52900 Ramat Gan Israel

We present a method for combining several segmentations of an image into a single one that in some sense is the average segmentation in order to achieve a more reliable and accurate segmentation result. The goal is to find a point in the "space of segmentations" which is close to all the individual segmentations. We present an algorithm for segmentation averaging. The image is first oversegmented into superpixels. Next, each segmentation is projected onto the superpixel map. An instance of the EM algorithm combined with integer linear programming is applied on the set of binary merging decisions of neighboring superpixels to obtain the average segmentation. Apart from segmentation averaging, the algorithm also reports the reliability of each segmentation. The performance of the proposed algorithm is demonstrated on manually annotated images from the Berkeley segmentation data set and on the results of automatic segmentation algorithms.

关键词： Image segmentation ensemble segmentation integer linear programming correlation clustering EM algorithm

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The Loading Optimization: A Novel integer linear programming Model

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ENTERPRISE INFORMATION SYSTEMS 2019年第10期13卷 1471-1482页

作者： Wang, Lei Ni, Mingfang Gao, Jie Shen, Qingguo Jia, Yongxing Yao, Changhua PLA Army Engn Univ Coll Commun Engn Nanjing Jiangsu Peoples R China JiLin Univ Zhuhai Coll Sch Disciplinary Bacs & Appl Stat Zhuhai Peoples R China

This paper establishes a new integer linear programming model for container loading problem. This model can be used to calculate the optimal loading plan for each container. To solve the model, in this paper, the model problem is decomposed into two easy to solve sub-problems: auxiliary problem (AP) and transportation problem, and prove by solving the two sub-problems can quickly and efficiently to find the optimal solution of the model. Finally, an example is given to illustrate the solution process, which shows that the algorithm can give the optimal stowage scheme quickly and effectively.

关键词： integer linear programming distribution optimisation transportation container

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An integer linear programming approach for a class of bilinear integer programs

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OPERATIONS RESEARCH LETTERS 2014年第3期42卷 226-230页

作者： Hu, Wuhua Tay, Wee Peng Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

We propose an integer linear programming (ILP) approach for solving integer programs with bilinear objectives and linear constraints. Our approach is based on finding upper and lower bounds for the integer ensembles in the bilinear objective function, and using the bounds to obtain a tight ILP reformulation of the original problem, which can then be solved efficiently. Numerical experiments suggest that the proposed approach outperforms a latest iterative ILP approach, with notable reductions in the average solution time. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Bilinear integer programming integer linear programming integer quadratic programming Binary binary linearization Binary integer linearization Bipartite graph

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Static and dynamic locality optimizations using integer linear programming

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2001年第9期12卷 922-941页

作者： Kandemir, M Banerjee, P Choudhary, A Ramanujam, J Ayguadé, E Penn State Univ Dept Comp Sci & Engn University Pk PA 16802 USA Northwestern Univ Dept Elect & Comp Engn Evanston IL 60208 USA Louisiana State Univ Dept Elect & Comp Engn Baton Rouge LA 70803 USA Univ Politecn Cataluna Dept Arquitectura Comp Ctr Europeu Parallelisme Barcelona 08034 Spain

The delivered performance on modern processors that employ deep memory hierarchies is closely related to the performance of the memory subsystem. Compiler optimizations aimed at improving cache locality are critical in realizing the performance potential of powerful processors. For scientific applications, several loop transformations have been shown to be useful in improving both temporal and spatial locality. Recently, there has been some work in the area of data layout optimizations, i.e., changing the memory layouts of multidimensional arrays from the language-defined default such as column-major storage in Fortran. The effect of such memory layout decisions is on the spatial locality characteristics of loop nests. While data layout transformations are not constrained by data dependences, they have no effect on temporal locality. On the other hand, loop transformations are not readily applicable to imperfect loop nests and are constrained by data dependences. More importantly, loop transformations affect the memory access patterns of all the arrays accessed in a loop nest and, as a result, the locality characteristics of some of the arrays may worsen. This paper presents a technique based on integer linear programming (ILP) that attempts to derive the best combination of loop and data layout transformations. Prior attempts to unify loop and data layout transformations for programs consisting of a sequence of loop nests have been based on heuristics not only for transformations for a single loop nest but also for the sequence in which loop nests will be considered. The ILP formulation presented here obviates the need for such heuristics and gives us a bar against which the heuristic algorithms can be compared, More importantly, our approach is able to transform memory layouts dynamically during program execution. This is particularly useful in applications whose disjoint code segments demand different layouts for a given array. In addition, we show how this formu

关键词： data reuse cache locality memory layouts compiler optimizations cache miss estimation integer linear programming

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Combining variable neighborhood search with integer linear programming for the generalized minimum spanning tree problem

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JOURNAL OF HEURISTICS 2008年第5期14卷 473-499页

作者： Hu, Bin Leitner, Markus Raidl, Guenther R. Vienna Univ Technol Inst Comp Graph & Algorithms A-1040 Vienna Austria

We consider the generalized version of the classical Minimum Spanning Tree problem where the nodes of a graph are partitioned into clusters and exactly one node from each cluster must be connected. We present a Variable Neighborhood Search (VNS) approach which uses three different neighborhood types. Two of them work in complementary ways in order to maximize search effectivity. Both are large in the sense that they contain exponentially many candidate solutions, but efficient polynomial-time algorithms are used to identify best neighbors. For the third neighborhood type we apply Mixed integer programming to optimize local parts within candidate solution trees. Tests on Euclidean and random instances with up to 1280 nodes indicate especially on instances with many nodes per cluster significant advantages over previously published metaheuristic approaches.

关键词： generalized minimum spanning tree variable neighborhood search dynamic programming integer linear programming

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