The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

A parallel block-based encryption schema for digital images using reversible cellular automata

In the modern era of secure communication, it is important to create uncertainty in the original data in order to avoid unauthorized entities to extract or manipulate information. From simple methods such as permutations of original data to different mapping algorithms, the security of the ciphers rely on the substitution process. There are many types of components proposed in literature that are evolved by different methodologies and ideas. The prevailing ciphers use substitution boxes (S-boxes) to do this transformation process. In this work, we present a literature review of the design, construction, and analysis of the S-boxes used in block ciphers. The performance of S-boxes depends on the design and algebraic structure used for the construction and is contingent upon its ability to resist against cryptanalysis. We present the details of the S-box synthesis process and issues pertaining to creating resistance against various types of attacks, and highlight the consequences of a particular design methodology. In the infancy of the development of modern block ciphers, Shannon (Bell Syst. Tech. J. 28(4):656–715, 1949) presented the idea of encryption with the implementation of substitution-permutation network (SPN). In this process, the data is initially transformed by the substation process and then permuted that ends the first round supported by the secret key for this step. This substitution-permutation process is repeated several times to ensure reliability of encrypted data. The objective of using the substitution-permutation network is to create confusion between cipher text and secret key, and add diffusion in the plaintext.

Literature survey on nonlinear components and chaotic nonlinear components of block ciphers

Data Mining and Knowledge Engineering.- EXiT-B: A New Approach for Extracting Maximal Frequent Subtrees from XML Data.- Synthetic Environment Representational Semantics Using the Web Ontology Language.- New Rules for Hybrid Spatial Reasoning.- Using Pre-aggregation for Efficient Spatial Query Processing in Sensor Environments.- Model Trees for Classification of Hybrid Data Types.- Finding Uninformative Features in Binary Data.- Knowledge Reduction of Rough Set Based on Partition.- Multiresolution Analysis of Connectivity.- Kernel Biased Discriminant Analysis Using Histogram Intersection Kernel for Content-Based Image Retrieval.- Unsupervised Image Segmentation Using Penalized Fuzzy Clustering Algorithm.- Multi-attributes Image Analysis for the Classification of Web Documents Using Unsupervised Technique.- Automatic Image Annotation Based on Topic-Based Smoothing.- A Focused Crawler with Document Segmentation.- An Intelligent Grading System Using Heterogeneous Linguistic Resources.- Probabilistic Data Generation for Deduplication and Data Linkage.- Mining Job Logs Using Incremental Attribute-Oriented Approach.- Dimensional Reduction of Large Image Datasets Using Non-linear Principal Components.- Classification by Instance-Based Learning Algorithm.- Analysis/Synthesis of Speech Signals Based on AbS/OLA Sinusoidal Modeling Using Elliptic Filter.- Robust Model Adaptation Using Mean and Variance Transformations in Linear Spectral Domain.- Using Support Vector Machine for Modeling of Pulsed GTAW Process.- Design of Simple Structure Neural Voltage Regulator for Power Systems.- EEG Source Localization for Two Dipoles in the Brain Using a Combined Method.- Intelligent Control of Micro Heat Exchanger with Locally Linear Identifier and Emotional Based Controller.- Identification of Anomalous SNMP Situations Using a Cooperative Connectionist Exploratory Projection Pursuit Model.- Learning Algorithms and Systems.- Neural Networks: A Replacement for Gaussian Processes?.- A Dynamic Merge-or-Split Learning Algorithm on Gaussian Mixture for Automated Model Selection.- Bayesian Radial Basis Function Neural Network.- An Empirical Study of Hoeffding Racing for Model Selection in k-Nearest Neighbor Classification.- Designing an Optimal Network Using the Cross-Entropy Method.- Generating Predicate Rules from Neural Networks.- Improving Ensembles with Classificational Cellular Automata.- A Gradient BYY Harmony Learning Algorithm on Mixture of Experts for Curve Detection.- A Novel Anomaly Detection Using Small Training Sets.- Induction of Linear Decision Trees with Real-Coded Genetic Algorithms and k-D Trees.- Intelligent Predictive Control of a 6-Dof Robotic Manipulator with Reliability Based Performance Improvement.- Sequential Search for Decremental Edition.- Bearing Similarity Measures for Self-organizing Feature Maps.- Efficient Spatial Clustering Algorithm Using Binary Tree.- Cluster Analysis of High-Dimensional Data: A Case Study.- Universal Clustering with Family of Power Loss Functions in Probabilistic Space.- Circular SOM for Temporal Characterisation of Modelled Gene Expressions.- Recursive Self-organizing Map as a Contractive Iterative Function System.- Differential Priors for Elastic Nets.- Graphics Hardware Implementation of the Parameter-Less Self-organising Map.- Weighted SOM-Face: Selecting Local Features for Recognition from Individual Face Image.- SOM-Based Novelty Detection Using Novel Data.- Multi-level Document Classifications with Self-organising Maps.- Bioinformatics.- Predictive Vaccinology: Optimisation of Predictions Using Support Vector Machine Classifiers.- Evolving Neural Networks for the Classification of Malignancy Associated Changes.- Matching Peptide Sequences with Mass Spectra.- Extraction by Example: Induction of Structural Rules for the Analysis of Molecular Sequence Data from Heterogeneous Sources.- A Multi-population ? 2 Test Approach to Informative Gene Selection.- Gene Selection of DNA Microarray Data Based on Regularization Networks.- Application of Mixture Models to Detect Differentially Expressed Genes.- A Comparative Study of Two Novel Predictor Set Scoring Methods.- Deriving Matrix of Peptide-MHC Interactions in Diabetic Mouse by Genetic Algorithm.- SVM Based Prediction of Bacterial Transcription Start Sites.- Exploiting Sequence Dependencies in the Prediction of Peroxisomal Proteins.- Protein Fold Recognition Using Neural Networks and Support Vector Machines.- Agents and Complex Systems.- Support Tool for Multi-agent Development.- A Hybrid Agent Architecture for Modeling Autonomous Agents in SAGE.- Toward Transitive Dependence in MAS.- An Architecture for Multi-agent Based Self-adaptive System in Mobile Environment.- Autonomous and Dependable Recovery Scheme in UPnP Network Settings.- A Transitive Dependence Based Social Reasoning Mechanism for Coalition Formation.- A Multi-agent Based Context Aware Self-healing System.- Combining Influence Maps and Cellular Automata for Reactive Game Agents.- Patterns in Complex Systems Modeling.- Global Optimization Using Evolutionary Algorithm Based on Level Set Evolution and Latin Square.- Co-evolutionary Rule-Chaining Genetic Programming.- A Dynamic Migration Model for Self-adaptive Genetic Algorithms.- Financial Engineering.- A Multicriteria Sorting Procedure for Financial Classification Problems: The Case of Business Failure Risk Assessment.- Volatility Modelling of Multivariate Financial Time Series by Using ICA-GARCH Models.- Volatility Transmission Between Stock and Bond Markets: Evidence from US and Australia.- A Machine Learning Approach to Intraday Trading on Foreign Exchange Markets.

Intelligent Data Engineering and Automated Learning - IDEAL 2005: 6th International Conference, Brisbane, Australia, July 6-8, 2005, Proceedings (Lecture Notes in Computer Science)

A cellular learning automata based algorithm for detecting community structure in complex networks

Substitution boxes (S-boxes) are the main elements of block ciphers, wide used in modern cryptography. In this paper we propose a new method to create S-boxes, which is based on application of Cellular Automata (CA). We present results of testing CA-based S-boxes, which confirm that CA are able to realize efficiently Boolean functions corresponding to classical S-boxes. Proposed CA-based S-boxes offer cryptographic properties comparable or better than classical S-box tables.

Cryptographically Strong S-Boxes Based on Cellular Automata

The problem of generation by cellular automata of high quality pseudorandom sequences useful in cryptography is considered in the paper For this purpose one dimensional nonuniform cellular automata is considered The quality of pseudorandom sequences generated by cellular automata depends on collective behavior of rules assigned to cellular automata cells Genetic algorithm is used to find suitable rules from predefined earlier set of rules It has been shown that genetic algorithm eliminates bad subsets of rules and founds subsets of rules, which provide high quality pseudorandom sequences These sequences are suitable for symmetric key cryptography and can be used in different cryptographic modules

Collective Behavior of Rules for Cellular Automata-based Stream Ciphers

This paper proposes a cellular automata-based solution of a binary classification problem. The proposed method is based on a two-dimensional, three-state cellular automaton (CA) with the von Neumann neighborhood. Since the number of possible CA rules (potential CA-based classifiers) is huge, searching efficient rules is conducted with use of a genetic algorithm (GA). Experiments show an excellent performance of discovered rules in solving the classification problem. The best found rules perform better than the heuristic CA rule designed by a human and also better than one of the most widely used statistical method: the k-nearest neighbors algorithm (k-NN). Experiments show that CAs rules can be successfully reused in the process of searching new rules.

Learning cellular automata rules for binary classification problem

Block ciphers are widely used in modern cryptography. Substitution boxes (S-boxes) are main elements of these types of ciphers. In this paper we propose a new method to create S-boxes, which is based on application of Cellular Automata (CA). We present the results of testing CA-based S-boxes. These results confirm that CA are able to realize efficiently the Boolean function corresponding to classical S-boxes the proposed CA-based S-boxes offer cryptographic properties comparable or better than classical S-box tables.

/pdf/designing-cryptographically-strong-s-boxes-with-the-use-of-uhh9zt69nu.pdf

Designing cryptographically strong S-boxes with the use of cellular automata

The most important elements of many block ciphers are nonlinear functions known as substitution boxes (S-boxes). Classical S-boxes are usually represented by numerical tables, which are used today in current cryptographic standards, such as Data Encryption Standard (DES) or Advanced Encryption Standard (AES), but in the result of developing methods of cryptanalysis they do not ensure enough safety of ciphers. Therefore, the open research issue now is to design new more sophisticated classes of S-boxes, in particular dynamic ones. In this paper we propose a methodology to design dynamic cellular automata (CA)-based S-boxes, which can be considered as a generator of CA-based S-boxes. We provide an exhaustive experimental analysis of the proposed CA-based S-boxes in terms of non-linearity, autocorrelation, balance and strict avalanche criterion. We show that the proposed S-boxes have high quality cryptographic properties (high non-linearity and balance, also low autocorrelation and distance to fulfill strict avalanche criterion). The interesting feature of the proposed S-boxes is a dynamic flexible structure, fully functionally realized by CA, while the classical S-boxes are represented by predefined unchangeable table structures.

Miroslaw Szaban

Papers

Cryptographically Strong S-Boxes Based on Cellular Automata

Collective Behavior of Rules for Cellular Automata-based Stream Ciphers

Learning cellular automata rules for binary classification problem

Designing cryptographically strong S-boxes with the use of cellular automata

Dynamic cellular automata-based s-boxes