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Biclique cryptanalysis of the full-round KLEIN block cipher

IET INFORMATION SECURITY 2015年第5期9卷 294-301页

作者： Ahmadian, Zahra Salmasizadeh, Mahmoud Aref, Mohammad Reza Shahid Beheshti Univ Dept Elect Engn Tehran Iran Sharif Univ Technol Elect Res Inst Tehran Iran Sharif Univ Technol Dept Elect Engn Informat Syst & Secur Lab Tehran Iran

In this study, the authors evaluate the security of the lightweight block cipher KLEIN-64 using biclique cryptanalysis. They first point out some new weaknesses of the diffusion layer and key schedule of this algorithm. Then they exploit them to cryptanalyst the full round version of KLEIN-64 using an asymmetric biclique. The (worst case) computations and data complexity of this cryptanalysis are 2(62.84) and 2(39), respectively. A modified version of this cryptanalysis is also presented which is slightly faster at the expense of the data required.

关键词： computational complexity cryptography full-round KLEIN block cipher KLEIN-64 biclique cryptanalysis diffusion layer key schedule asymmetric biclique data complexity

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Biclique-based cryptanalysis of the block cipher SQUARE

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IET INFORMATION SECURITY 2014年第3期8卷 207-212页

作者： Mala, Hamid Univ Isfahan Dept Informat Technol Engn Esfahan Iran

SQUARE, an eight-round substitution-permutation block cipher, is considered as a predecessor of the advanced encryption standard (AES). Recently, the concept of biclique-based key recovery of block ciphers was introduced and applied to full-round versions of three variants of AES. In this paper, this technique is applied to analyse the block cipher SQUARE. First, a biclique for three rounds of SQUARE using independent related-key differentials has been found. Then, an attack on this cipher is presented, with a data complexity of about 2(48) chosen plaintexts and a time complexity of about 2(125.7) encryptions. The attack is the first successful attack on full-round SQUARE in the single-key scenario.

关键词： advanced encryption standard Computational complexity cryptography computational complexity time complexity independent related-key differentials biclique-based key recovery block cipher SQUARE data security eight-round substitution-permutation block cipher biclique-based cryptanalysis Cryptography AES data complexity block ciphers

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Success probability of multiple/multidimensional linear cryptanalysis under general key randomisation hypotheses

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CRYPTOGRAPHY AND COMMUNICATIONS-DISCRETE-STRUCTURES BOOLEAN FUNCTIONS AND SEQUENCES 2018年第5期10卷 835-879页

作者： Samajder, Subhabrata Sarkar, Palash Indian Stat Inst Appl Stat Unit 203 BT Rd Kolkata 700108 India

This work considers statistical analysis of attacks on block cyphers using several linear approximations. A general and unified approach is adopted. To this end, the general key randomisation hypotheses for multidimensional and multiple linear cryptanalysis are introduced. Expressions for the success probability in terms of the data complexity and the advantage are obtained using the general key randomisation hypotheses for both multidimensional and multiple linear cryptanalysis and under the settings where the plaintexts are sampled with or without replacement. Particularising to standard/adjusted key randomisation hypotheses gives rise to success probabilities in 16 different cases out of which in only five cases expressions for success probabilities have been previously reported. Even in these five cases, the expressions for success probabilities that we obtain are more general than what was previously obtained. A crucial step in the analysis is the derivation of the distributions of the underlying test statistics. Whilst we carry out the analysis formally to the extent possible, there are certain inherently heuristic assumptions that need to be made. In contrast to previous works which have implicitly made such assumptions, we carefully highlight these and discuss why they are unavoidable. Finally, we provide a complete characterisation of the dependence of the success probability on the data complexity.

关键词： Multidimensional linear cryptanalysis Multiple linear cryptanalysis Chi-squared distribution Success probability data complexity Advantage

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DIAGNOSE EFFECTIVE EVOLUTIONARY PROTOTYPE SELECTION USING AN OVERLAPPING MEASURE

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2009年第8期23卷 1527-1548页

作者： Garcia, Salvador Cano, Jose-Ramon Bernado-Mansilla, Ester Herrera, Francisco Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain Univ Jaen Higher Polytech Ctr Linares Dept Comp Sci Linares 23700 Spain Univ Ramon Llull Dept Comp Engn Barcelona 08022 Spain

Evolutionary prototype selection has shown its effectiveness in the past in the prototype selection domain. It improves in most of the cases the results offered by classical prototype selection algorithms but its computational cost is expensive. In this paper, we analyze the behavior of the evolutionary prototype selection strategy, considering a complexity measure for classification problems based on overlapping. In addition, we have analyzed different k values for the nearest neighbour classifier in this domain of study to see its influence on the results of PS methods. The objective consists of predicting when the evolutionary prototype selection is effective for a particular problem, based on this overlapping measure.

关键词： Prototype selection evolutionary prototype selection complexity measures overlapping measure data complexity

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Functional queries in datalog

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NEW GENERATION COMPUTING 2002年第4期20卷 339-371页

作者： Basta, S Flesca, S Greco, S CNR ISI I-87036 Arcavacata Di Rende Italy Univ Calabria DEIS I-87036 Arcavacata Di Rende Italy

A 'functional' query is a query whose answer is always defined and unique i.e. it is either true or false in all models. It has been shown that the expressive powers of the various types of stable models, when restricted to the class of dataLOG functional queries, do not in practice go beyond those of well-founded semantics, except for the least undefined stable models which, instead, capture the whole boolean hierarchy BH. In this paper we present a 'functional' language which, by means of a disciplined use of negation, achieves the desired level of expressiveness up to BH. Although the semantics of the new language is partial, all atoms in the source program are defined and possibly undefined atoms are introduced in a rewriting phase to increase the expressive power. We show that the language satisfies 'desirable' properties better than classical languages with (unstratified) negation and stable model semantics. We present an algorithm for the evaluation of functional queries and we show that exponential time resolution is required for hard problems only. Finally we present the architecture of a prototype of the language which has been developed.

关键词： logic programming datalog stable models functional queries expressive power data complexity

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Advanced Machine Learning Based Malware Detection Systems

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IEEE ACCESS 2024年 12卷 115296-115305页

作者： Kim, Song-Kyoo Feng, Xiaomei Al Hamadi, Hussam Damiani, Ernesto Yeun, Chan Yeob Nandyala, Sivaprasad Macao Polytech Univ Fac Appl Sci Macau Peoples R China Univ Dubai Engn & Informat Technol Dubai U Arab Emirates Khalifa Univ Sci & Technol Ctr Cyber Phys Syst EECS Dept Abu Dhabi U Arab Emirates Technol Innovat Inst Secure Syst Res Ctr Abu Dhabi U Arab Emirates

In the area of machine learning (ML) training data optimization through the construction of compact data, the focus of this paper is presented. The concept of compact data design, aimed at creating an optimized dataset that maximizes benefits without the need to manage a vast amount of complex data, is introduced. Improvements in the methods for optimizing ML training have been incorporated into the development of artificial intelligence (AI) systems. The introduction of understanding ML training datasets as a facet of Explainable AI (XAI), comprehensible to humans, has been made. Among the methods of XAI, the evaluation of input feature importance stands out as a way to enhance the accuracy of complex ML models. The innovative method of compact data design for optimizing ML training through dataset reduction is proposed. The performance of an ML-based malware detection system, along with its variant utilizing compact data, has been assessed, demonstrating the maintenance of 99% accuracy. By applying a 76% reduced input dataset, the speed of ML training with the novel compact data design could be maximized, suggesting that an ML system trained in this manner could achieve statistically equivalent accuracy with only 57% of the original data sample size.

关键词： Machine learning Malware Feature extraction Machine learning algorithms Correlation Accuracy Supervised learning Classification algorithms data analysis Artificial intelligence Compact data data reduction machine learning malware security data complexity artificial intelligence supervised learning robust classification

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ERROR complexity ANALYSIS OF ALGORITHMS FOR MATRIX MULTIPLICATION AND MATRIX CHAIN PRODUCT

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IEEE TRANSACTIONS ON COMPUTERS 1981年第10期30卷 758-771页

作者： TSAO, NK Department of Computer Science Wayne State University

The error complexity analysis of three algorithms for matrix multiplication and matrix chain product has been given. It is shown that the usual inner product type algorithm is by far the best algorithm for simple matrix multiplication or matrix chain product in terms of minimal basic term growth and minimal error complexities, the latter being independent of the order of pairwise matrix multiplications. Winograd's algorithm is comparable to the usual one, although in matrix chain product the error and data complexities are very sensitive to the order of pairwise matrix multiplication. Strassen's algorithm is not very attractive numerically for having the largest upper bound for both the maximum error complexity and the number of basic terms generated.

关键词： Winograds Algorithm data complexity Error complexity Floating Point Arithmetic Matrix Chain Product Matrix Multiplication Strassens Algorithm

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Role of visual analytics in supporting mental healthcare systems research and policy: A systematic scoping review

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INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2020年 50卷 17-27页

作者： Chung, Younjin Bagheri, Nasser Alberto Salinas-Perez, Jose Smurthwaite, Kayla Walsh, Erin Furst, MaryAnne Rosenberg, Sebastian Salvador-Carulla, Luis Australian Natl Univ Coll Hlth & Med Res Sch Populat Hlth Ctr Mental Hlth Res 63 Eggleston Rd Acton ACT 2601 Australia Univ Loyola Andalucia Dept Quantitat Methods Calle Energia Solar 1 Seville 41014 Spain Australian Natl Univ Coll Hlth & Med Res Sch Populat Hlth Dept Hlth Serv Res & Policy 62 Mills Rd Acton ACT 2601 Australia Australian Natl Univ Coll Hlth & Med Res Sch Populat Hlth Ctr Res Ageing Hlth & Wellbeing 62 Mills Rd Acton ACT 2601 Australia Univ Sydney Brain & Mind Ctr 94 Mallett St Camperdown NSW 2050 Australia

The availability of healthcare data has exponentially grown, both in quantity and complexity. The speed of this evolution has generated new challenges for translating complex data into effective evidence-informed policy. Visual analytics offers new capacity to analyze healthcare systems and support better decision-making. We conducted a systematic scoping review to look for evidence of visual analytics approaches being applied to mental healthcare systems and their use in driving policy. We found 79 relevant studies and categorized them in two ways: by study purpose and by type of visualization. The majority (67.1%) of the studies used geographical maps, and 11% conducted highly complex studies requiring novel visualizations. Significantly, only 15% of the studies provided information indicating high levels of usability for policy and planning. Our findings suggest that while visual analytics continues to evolve rapidly, there is a need to ensure this evolution reflects the practical needs of policy makers.

关键词： Systematic scoping review Mental healthcare system data complexity Visualization Visual analytics Evidence-informed decision-making

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A unifying view of class overlap and imbalance: Key concepts, multi-view panorama, and open avenues for research

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INFORMATION FUSION 2023年 89卷 228-253页

作者： Santos, Miriam Seoane Abreu, Pedro Henriques Japkowicz, Nathalie Fernandez, Alberto Santos, Joao Univ Coimbra Dept Informat Engn CISUC P-3030290 Coimbra Portugal Amer Univ Dept Comp Sci Washington DC 20016 USA Univ Granada Andalusian Res Inst Data Sci & Computat Intellige Dept Comp Sci & Artificial Intelligence DaSCI Granada Spain Univ Porto Inst Ciencias Biomed Abel Salazar Porto Portugal IPO Porto Res Ctr CI IPOP Porto Portugal

The combination of class imbalance and overlap is currently one of the most challenging issues in machine learning. While seminal work focused on establishing class overlap as a complicating factor for classification tasks in imbalanced domains, ongoing research mostly concerns the study of their synergy over real-word applications. However, given the lack of a well-formulated definition and measurement of class overlap in real-world domains, especially in the presence of class imbalance, the research community has not yet reached a consensus on the characterisation of both problems. This naturally complicates the evaluation of existing approaches to address these issues simultaneously and prevents future research from moving towards the devise of specialised solutions. In this work, we advocate for a unified view of the problem of class overlap in imbalanced domains. Acknowledging class overlap as the overarching problem - since it has proven to be more harmful for classification tasks than class imbalance - we start by discussing the key concepts associated to its definition, identification, and measurement in real-world domains, while advocating for a characterisation of the problem that attends to multiple sources of complexity. We then provide an overview of existing data complexity measures and establish the link to what specific types of class overlap problems these measures cover, proposing a novel taxonomy of class overlap complexity measures. Additionally, we characterise the relationship between measures, the insights they provide, and discuss to what extent they account for class imbalance. Finally, we systematise the current body of knowledge on the topic across several branches of Machine Learning (data Analysis, data Preprocessing, Algorithm Design, and Meta-learning), identifying existing limitations and discussing possible lines for future research.

关键词： Class imbalance Imbalanced data Class overlap data complexity data intrinsic characteristics complexity measures

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Use of Nonlinear Features for Automated Characterization of Suspicious Ovarian Tumors Using Ultrasound Images in Fuzzy Forest Framework

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2018年第4期20卷 1385-1402页

作者： Acharya, U. Rajendra Akter, Ayesha Chowriappa, Pradeep Dua, Sumeet Raghavendra, U. Koh, Joel E. W. Tan, Jen Hong Leong, Sook Sam Vijayananthan, Anushya Hagiwara, Yuki Ramli, Marlina Tanty Ng, Kwan Hoong Ngee Ann Polytech Dept Elect & Comp Engn Singapore 599489 Singapore SUSS Univ Sch Sci & Technol Dept Biomed Engn Singapore 599491 Singapore Univ Malaya Dept Biomed Engn Fac Engn Kuala Lumpur Malaysia Louisiana Tech Univ Comp Sci Ruston LA 71270 USA Manipal Univ Manipal Inst Technol Dept Instrumentat & Control Engn Manipal 576104 Karnataka India Univ Malaya Dept Biomed Imaging Fac Med Kuala Lumpur 50603 Malaysia Univ Malaya Res Imaging Ctr Fac Med Kuala Lumpur 50603 Malaysia Univ Teknol MARA Fac Med Med Imaging Unit Sungai Buloh Malaysia

Ovarian cancer is one of the prime causes of mortality in women. Diagnosis of ovarian cancer using ultrasonography is tedious as ovarian tumors exhibit minute clinical and structural differences between the suspicious and non-suspicious classes. Early prediction of ovarian cancer will reduce its growth rate and may save many lives. Computer-aided diagnosis (CAD) is a noninvasive method for finding ovarian cancer in its early stage which can avoid patient anxiety and unnecessary biopsy. This study investigates the efficacy of a novel CAD tool to characterize suspicious ovarian cancer using Radon-transformed nonlinear features. The obtained dimension of the extracted features is reduced using Relief-F feature selection method. In this study, we have employed the fuzzy forest-based ensemble classifier in contrast to the known crisp rule-based classifiers. The proposed method is evaluated using 469 (non-suspicious: 238, suspicious: 231) subjects and achieved a maximum 80.60 +/- 0.5% accuracy, 81.40% sensitivity, 76.30% specificity with fuzzy forest, an ensemble fuzzy classifier using thirty-nine features. The proposed method is robust and reproducible as it uses maximum number subjects (469) as compared to state-of-the-art techniques. Hence, it can be used as an assisting tool by gynecologists during their routine screening.

关键词： Ovarian cancer Releif-F data complexity k-NN Fuzzy forest Random forest

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