Chaotic encryption method based on life-like cellular automata

Machicao, Jeaneth; Marco, Anderson G.; Bruno, Odemir Martinez

doi:10.1016/j.eswa.2012.05.020

Cited by 38 publications

(27 citation statements)

References 29 publications

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“…The choice of the Life-like family was due to the flexibility of these CAs which provide a vast rule space555657. CAs are usually represented on regular tessellations (square grids) in n-dimensional Euclidean spaces, , and the set of transition rules, Φ, is defined over a fixed number of neighbors.…”

Section: Resultsmentioning

confidence: 99%

Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks

Miranda

Machicao

Bruno

2016

Sci Rep

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Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability.

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Section: Resultsmentioning

confidence: 99%

Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks

Miranda

Machicao

Bruno

2016

Sci Rep

Self Cite

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“…Os modelos que utilizam ACs na criptografia com regras sensíveis foi explorado em Sen [22], no entanto o paralelismo e a segurança desses modelos é limitada, devido ao fato da propriedade aditiva das regras, que não as tornam caóticas. Sistemas criptográficos de boa qualidade devem retornar cifras embaralhadas e caóticas Wolfram [26], Machicao [14], dificultando a criptoanálise.…”

Section: Criptografia Baseada Em Autômatos Celularesunclassified

Autômatos celulares unidimensionais caóticos com borda fixa aplicados à modelagem de um sistema criptográfico para imagens digitais

Silva

Soares²,

Lima

2016

RITA

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Resumo: O principal objetivo da criptografia de dados é possibilitar que duas entidades se comuniquem ao longo de um canal inseguro, de tal forma que nenhuma outra entidade consiga decifrar a mensagem que é enviada. Muitos métodos de criptografia clássica já foram investigados para minimizar este problema. Uma nova abordagem para este tema são os Autômatos Celulares (ACs), atualmente estudados por sua capacidade de processar grandes volumes de dados em paralelo. Nesse trabalho é investigado um novo modelo de autômato celular para criptografia de imagens, que tem como característica o uso do cálculo de pré-imagens a partir de chaves caóticas. O modelo é denominado Border Chaotic Cellular Automata (BCCA) para cifragem. Resultados mostraram que o modelo tem grande potencial para a realização de cifragem de grandes volumes de dados.Palavras-chave: autômatos celulares, cálculo de pré-imagens, criptografia de imagens, processamento paralelo

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“…Definition 3 A discrete-space cellular automata can be represented as a sextuple Ω = (S, I, f, f 0 , η, φ), where [44]:…”

Section: Random Fields Dynamics As Non-deterministic Cellular Automatamentioning

confidence: 99%

Information geometry, simulation and complexity in Gaussian random fields

Levada

2016

Monte Carlo Methods and Applications

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Random fields are useful mathematical objects in the characterization of non-deterministic complex systems. A fundamental issue in the evolution of dynamical systems is how intrinsic properties of such structures change in time. In this paper, we propose to quantify how changes in the spatial dependence structure affect the Riemannian metric tensor that equips the model's parametric space. Defining Fisher curves, we measure the variations in each component of the metric tensor when visiting different entropic states of the system. Simulations show that the geometric deformations induced by the metric tensor in case of a decrease in the inverse temperature are not reversible for an increase of the same amount, provided there is significant variation in the system's entropy: the process of taking a system from a lower entropy state A to a higher entropy state B and then bringing it back to A, induces a natural intrinsic one-way direction of evolution. In this context, Fisher curves resemble mathematical models of hysteresis in which the natural orientation is pointed by an arrow of time.

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Chaotic encryption method based on life-like cellular automata

Cited by 38 publications

References 29 publications

Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks

Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks

Autômatos celulares unidimensionais caóticos com borda fixa aplicados à modelagem de um sistema criptográfico para imagens digitais

Information geometry, simulation and complexity in Gaussian random fields

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