检索结果-内蒙古大学图书馆

Improved impossible differential attack on reduced version of Camellia with FL/FL^-1 functions

IET INFORMATION SECURITY 2016年第6期10卷 425-432页

作者： Liu, Ya Yang, Anren Liu, Zhiqiang Li, Wei Wang, Qingju Song, Liang Gu, Dawu Univ Shanghai Sci & Technol Dept Comp Sci & Engn Shanghai 200093 Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Donghua Univ Sch Comp Sci & Technol Shanghai 201620 Peoples R China Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur Beijing 100093 Peoples R China Shanghai Key Lab Integrate Adm Technol Informat S Shanghai 200240 Peoples R China Katholieke Univ Leuven ESAT COSIC B-3001 Leuven Belgium Katholieke Univ Leuven iMinds B-3001 Leuven Belgium

As an ISO/IEC international standard, Camellia has been used in various cryptographic applications. In this study, the authors present the best currently known attacks on Camellia-192/256 with key-dependent layers FL/FL-1 (without the whitening layers) by taking advantage of the intrinsic weakness of keyed functions, the redundancy of key schedule and the early abort technique. Specifically, the authors mount the first impossible differential attack on 13-round Camellia-192 with 2(124.79) chosen plaintexts, 2(186.09) 13-round encryptions and 2(129.79) bytes, while the analysis for the biggest number of rounds in previous results on Camellia-192 worked on 12 rounds. Furthermore, the authors successfully attack on 14-round Camellia-256 with 2(122.14) chosen plaintexts, 2(228.33) 14-round encryptions and 2(134.14) bytes. Compared with the previously best known attack on 14-round Camellia-256, the time and memory complexities are reduced by 2(9.87) times and 2(46.06) times, and the data complexity is comparable.

关键词： ISO standards IEC standards cryptography impossible differential attack Camellia standard ISO international standard IEC international standard cryptographic applications Camellia-192 Camellia-256 time complexity memory complexity data complexity

来源：评论

学校读者我要写书评

暂无评论

Temporal query entailment in the Description Logic SHQ

引用

JOURNAL OF WEB SEMANTICS 2015年 33卷 71-93页

作者： Baader, Franz Borgwardt, Stefan Lippmann, Marcel Tech Univ Dresden Inst Theoret Comp Sci D-01062 Dresden Germany

Ontology-based data access (OBDA) generalizes query answering in databases towards deductive entailment since (i) the fact base is not assumed to contain complete knowledge (i.e., there is no closed world assumption), and (ii) the interpretation of the predicates occurring in the queries is constrained by axioms of an ontology. OBDA has been investigated in detail for the case where the ontology is expressed by an appropriate Description Logic (DL) and the queries are conjunctive queries. Motivated by situation awareness applications, we investigate an extension of OBDA to the temporal case. As the query language we consider an extension of the well-known propositional temporal logic LTL where conjunctive queries can occur in place of propositional variables, and as the ontology language we use the expressive DL SHQ. For the resulting instance of temporalized OBDA, we investigate both data complexity and combined complexity of the query entailment problem. In the course of this investigation, we also establish the complexity of consistency of Boolean knowledge bases in SHQ. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Description Logic Ontology-based data access Linear temporal logic complexity data complexity

来源：评论

学校读者我要写书评

暂无评论

Improved integral attacks without full codebook

引用

IET INFORMATION SECURITY 2018年第6期12卷 513-520页

作者： Chu, Zhihui Chen, Huaifeng Wang, Xiaoyun Li, Lu Dong, Xiaoyang Ding, Yaoling Hao, Yonglin Shandong Univ Key Lab Cryptol Technol & Informat Secur Minist Educ Jinan 250100 Shandong Peoples R China Shandong Univ Sch Math Jinan 250100 Shandong Peoples R China Tsinghua Univ Inst Adv Study Beijing 100084 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China

The integral attack, exploits the balanced property of the output in the distinguisher. Usually, adversaries append some rounds after the distinguisher, guess the corresponding key bits and check whether the target bits are balanced. Few works add rounds before the distinguisher to make the key recovery attack. In the first full-round attack on MISTY1, Todo adds one FL layer (key-dependent linear function) before the distinguisher. In this study, the authors extend his method and give a general method, which they can use to extend some rounds (non-linear) before the distinguisher to attack more rounds with data complexity smaller than the whole space and little extra time consumption. The basic idea is that for different subkeys guessed in the forward rounds, they set different constant values for the input of the distinguisher. Finally, the selected data space is not full. For substitution permutation network (SPN) (Feistel with SPN round function) structures with 4bit S-box and bit permutation, they estimate the data complexity when adding one round before the distinguishers for all 4bit S-boxes. Using the method, they improve the integral attacks on PRESENT, RECTANGLE, TWINE and LBlock, and their results could cover one more round.

关键词： cryptography FL layer SPN round function key bits bit permutation forward rounds data complexity key-dependent linear function key recovery attack target bits distinguisher improved integral attacks

来源：评论

学校读者我要写书评

暂无评论

Adaptive pattern recognition

引用

JOURNAL OF MATHEMATICAL IMAGING AND VISION 1997年第4期7卷 325-339页

作者： Johansen, P Department of Computer Science University of Copenhagen Copenhagen Denmark

A long term goal of research in artificial intelligence is to determine and to implement principles which permit a movable machine to direct its actions depending upon sensory feed-back from its environment. This paper concentrates on spatial sensors which input images (2-dimensional arrays). A proposal is put forward in which the machine adapts to the actual data, and examples are given of input prediction, of detection of unexpected events, and of recognition of spatial patterns. The image sequence is locally partitioned into temporally contiguous subsequences of a fixed spatial extent. The spatial extent is constant over time and the temporal extent of a subsequence is maximized subject to the condition that the subsequence has occurred previously. The principle is illustrated on image sequences. It is further demonstrated on images which are structured as pseudo-temporal sequences of their rows. The demonstrations use diverse complex and simple examples to illustrate the versatility of the method. The demonstrations show that to a large degree it is not necessary for the user to supply explicit models for different pattern recognition tasks.

关键词： pattern recognition image processing data complexity prediction surveillance

来源：评论

学校读者我要写书评

暂无评论

DDES: A Distribution-Based Dynamic Ensemble Selection Framework

引用

IEEE ACCESS 2021年 9卷 40743-40754页

作者： Choi, Ye-Rim Lim, Dong-Joon Sungkyunkwan Univ Dept Ind Engn Suwon 16419 South Korea

Dynamic Ensemble Selection (DES) is a special type of ensemble modeling that selects different subsets of base classifiers for different test sample cases. In this process, multiple base classifiers are compared in terms of their competence as an ensemble to the best possible prediction for a given test sample case. Traditional DES methods rely on the Euclidean distance-based k-Nearest Neighbor (kNN) algorithm to identify the most relevant reference data points in such a way that the base classifiers correctly classifying them can be considered for an optimal ensemble. However, this approach is sensitive to the local structure of the data and the presence of noisy or irrelevant attributes. This study proposes a novel distribution-based DES (DDES) framework that takes the data structure into consideration when selecting reference data points. The experimental results for 30 classification problems indicate that the proposed approach yielded the best accuracy among the competing DES methods. Additionally, we discuss the correlation between the data complexity and the improvement in classification performance.

关键词： Classification algorithms Diversity reception Prediction algorithms Noise measurement Licenses Indexes Heuristic algorithms Multiple classifier systems dynamic ensemble selection region of competence feature variance data complexity

来源：评论

学校读者我要写书评

暂无评论

complexity evaluation of benchmark instances for the p-median problem

引用

MATHEMATICAL AND COMPUTER MODELLING 2011年第9-10期53卷 1719-1736页

作者： Goldengorin, B. Krushinsky, D. Univ Groningen Dept Operat NL-9700 AV Groningen Netherlands Nizhny Novgorod Branch Dept Appl Math & Informat Higher Sch Econ Nizhnii Novgorod 603155 Russia

The paper is aimed at experimental evaluation of the complexity of the p-Median problem instances, defined by m x n costs matrices, from several of the most widely used libraries. The complexity is considered in terms of possible problem size reduction and preprocessing, irrespective of the solution algorithm. We use a pseudo-Boolean representation of PMP instances that allows several reduction techniques to be applied in a straightforward way: combining similar monomials in the polynomial, truncation of the polynomial from degree (m-1) to (m-p) implying costs matrix truncation and exclusion of some rows from the costs matrix ( preprocessing based only on compactification of the costs matrix), decomposition of the polynomial into the minimum number of expressions inducing the minimum number of aggregated columns ( reduction of the columns' number in the costs matrix). We show that the reduced instance has at most E(i=1)(m-p) min{n, (m)(l))} + 1 nonzero entries. We also provide results of computational experiments with the mentioned reductions that allow classification of the benchmark data complexity. Finally, we propose a new benchmark library of instances not amenable to size reduction by means of data compactification. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： p-Median problem Pseudo-Boolean polynomial Problem size reduction data complexity

来源：评论

学校读者我要写书评

暂无评论

Modeling the combined influence of complexity and quality in supervised learning

引用

INTELLIGENT data ANALYSIS 2022年第5期26卷 1247-1274页

作者： Mendes, Rene de Avila Augusto da Silva, Leandro Univ Presbiteriana Mackenzie BigMAAp Sao Paulo Brazil Univ Presbiteriana Mackenzie FCI Sao Paulo Brazil

data classification is a data mining task that consists of an algorithm adjusted by a training dataset that is used to predict an object's class (unclassified) on analysis. A significant part of the performance of the classification algorithm depends on the dataset's complexity and quality. data complexity involves the investigation of the effects of dimensionality, the overlap of descriptive attributes, and the classes' separability. data Quality focuses on the aspects such as noise data (outlier) and missing values. The factors data complexity and data Quality are fundamental for the performance of classification. However, the literature has very few studies on the relationship between these factors and to highlight their significance. This paper applies Structural Equation Modeling and the Partial Least Squares Structural Equation Modeling (PLS-SEM) algorithm and, in an innovative manner, associates data complexity and data Quality contributions to Classification Quality. Experimental analysis with 178 datasets obtained from the OpenML repository showed that the control of complexity improves the classification results more than data quality does. Additionally paper also presents a visual tool of datasets analysis about the classification performance perspective in the dimensions proposed to represent the structural model.

关键词： Structural Equation Modeling data complexity data Quality supervised learning

来源：评论

学校读者我要写书评

暂无评论

Robust analysis and optimization of a novel efficient quality assurance model in data warehousing

引用

COMPUTERS & ELECTRICAL ENGINEERING 2019年 74卷 233-244页

作者： Amuthabala, P. Santhosh, R. Karpagam Acad Higher Educ Dept Comp Sci & Engn Coimbatore Tamil Nadu India

The significance of distributed data warehouses is to initiate the proliferation of various analytical applications. However, with the increase of ubiquitous devices, it is likely that massive volumes of data will be generated, which poses further problems based on the degradation of data quality. The practical reasons for the degradation of data quality in distributed warehouses are identified as heterogeneous data, uncertain inferior data which further affect predictions. The proposed system presents an integrated optimization model to address all the quality degradation problems and to provide a better computational model which effectively incorporates a higher degree of quality assurance. An analytical methodology is adopted in order to develop the proposed quality assurance model for distributed data warehouses. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： data warehouse Distributed data complexity data quality Quality assurance Optimization Machine learning

来源：评论

学校读者我要写书评

暂无评论

Chosen-plaintext linear attacks on Serpent

引用

IET INFORMATION SECURITY 2013年第4期7卷 293-299页

作者： Huang, Jialin Lai, Xuejia Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200030 Peoples R China

In this study, the authors consider chosen-plaintext variants of the linear attack on reduced round Serpent. By reasonably fixing parts of the plaintexts of 10-round Serpent the number of texts required in a linear attack with single approximation can be significantly reduced by a factor of 2(22). The authors also give the best data complexity on 10-round Serpent so far, which is 2(80). Moreover, the authors extend the chosen-plaintext technique to the linear attack using multiple approximations and improve the results of cryptanalysis in data complexity or/and time complexity in different scenarios. As an application to show the usefulness of this technique, an experiment in the multidimensional linear model on 5-round Serpent is given.

关键词： approximation theory computational complexity cryptography text analysis chosen-plaintext linear attack reduced round Serpent 10-round Serpent data complexity multiple approximations cryptanalysis time complexity multidimensional linear model 5-round Serpent

来源：评论

学校读者我要写书评

暂无评论

A novel ensemble method for classification in imbalanced datasets using split balancing technique based on instance hardness (sBal_IH)

引用

NEURAL COMPUTING & APPLICATIONS 2021年第17期33卷 11233-11254页

作者： Chongomweru, Halimu Kasem, Asem Univ Teknol Brunei Sch Comp & Informat Bandar Seri Begawan Brunei

Classification tasks in datasets that suffer from high class imbalance pose challenge to machine learning algorithms and such datasets are prevalent in many real-world domains and applications. In machine learning research, ensemble methods for classification tasks in imbalanced datasets have attracted a lot of attention due to their ability to improve classification performance. However, these methods are still prone to the negative effects of noise in the training sets. Furthermore, many of them alter the original class distribution to create a sort of balance in the datasets through over-sampling or undersampling techniques, which can lead to overfitting or discarding useful data, respectively, and thus may still hamper performance. In this work, we propose a novel ensemble method for classification that creates an arbitrary number of balanced splits (sBal) of data generated based on Instance Hardness as a weighting mechanism for creating balanced bags. Each of the generated bags will contain all the minority instances, and a mixture of majority instances with varying degrees of hardness (easy, normal, and hard), and we call this approach sBal_IH technique. This will enable base learners to train on different balanced bags comprising varied characteristics of the training data. We evaluated the performance of our proposed method on a total of 100 datasets that include 30 synthetic datasets with controlled levels of noise, 29 balanced and 41 imbalanced real-world datasets, and compared its performance with both traditional ensemble methods (Bagging, Wagging Random Forest, and AdaBoost), and those specialized for class imbalanced problems (Balanced Bagging, Balanced Random forest, RUSBoost, and Easy Ensemble). The results reveal that our proposed method brings a substantial improvement in classification performance relevant to the compared methods. For statistical significance analysis, we conducted Friedman's nonparametric statistical test with Bergman post hoc te

关键词： Imbalanced data Binary classification Instance hardness data complexity Ensemble Split balancing Balanced bagging Balanced random forest Easy ensemble Wagging

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：