On Application of Neural Networks for S-Boxes Design

Kotlarz, Piotr; Kotulski, Zbigniew

doi:10.1007/11495772_38

Cited by 8 publications

(5 citation statements)

References 5 publications

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“…Lately, in [91,115,116] some techniques for the improvement of the previously proposed protocol were presented. Finally, the idea of applying ANNs for the construction of S-boxes was presented in [60].…”

Section: Review Of Cryptography and Cryptanalysis Through Computationmentioning

confidence: 99%

Cryptography and Cryptanalysis Through Computational Intelligence

Laskari

Meletiou

Stamatiou

et al. 2007

Studies in Computational Intelligence

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Section: Review Of Cryptography and Cryptanalysis Through Computationmentioning

confidence: 99%

Cryptography and Cryptanalysis Through Computational Intelligence

Laskari

Meletiou

Stamatiou

et al. 2007

Studies in Computational Intelligence

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“…The higher dimension multilayer perceptron has very complex nonlinear dynamic behaviors, which contain various functions (e.g., activation function and step function). As Piotr et al remarked [ 14 ], securely enhance multilayer perceptron must satisfy certain conditions, the higher the dimension of S-box, the more statistical analysis it can be applied to multilayer perceptron, which is complex for algorithm designers and malicious password analysts. Yet, the carefully designed S-box is just a strong nonlinear Boolean (vector) map with avalanche characteristics.…”

Section: Introductionmentioning

confidence: 99%

Novel Low-Power Construction of Chaotic S-Box in Multilayer Perceptron

Ren

Yang

et al. 2022

Entropy

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Multilayer perceptron is composed of massive distributed neural processors interconnected. The nonlinear dynamic components in these processors expand the input data into a linear combination of synapses. However, the nonlinear mapping ability of original multilayer perceptron is limited when processing high complexity information. The introduction of more powerful nonlinear components (e.g., S-box) to multilayer perceptron can not only reinforce its information processing ability, but also enhance the overall security. Therefore, we combine the methods of cryptography and information theory to design a low-power chaotic S-box (LPC S-box) with entropy coding in the hidden layer to make the multilayer perceptron process information more efficiently and safely. In the performance test, our S-box architecture has good properties, which can effectively resist main known attacks (e.g., Berlekamp Massey-attack and Ronjom–Helleseth attack). This interdisciplinary work can attract more attention from academia and industry to the security of multilayer perceptron.

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“…The term itself was first time used by Dourlens [13] in 1995. The neural networks are exploited to the realization of the cryptographic operations such as the S-BOX operation [14]. In addition they can be used in cryptographic context as a supporting tool e.g.…”

Section: Introductionmentioning

confidence: 99%

The Impact of the TPM Weights Distribution on Network Synchronization Time

Dolecki

Kozera

2015

Lecture Notes in Computer Science

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Part 7: Various Aspects of Computer SecurityInternational audienceTwo neural networks with randomly chosen initial weights may achieve the same weight vectors in the process of their mutual learning. This phenomenon is called a network synchronization, and can be used in cryptography to establish the keys for further communication. The time required to achieve consistent weights of networks depends on the initial similarity and on the size of the network. In the previous work related to this topic the weights in TPM networks are randomly chosen and no detailed research on used distribution is performed. This paper compares the synchronization time obtained for the network weights randomly chosen from either the uniform distribution or from the Gaussian distribution with different values of standard deviation. The synchronization time of the network is examined here as a function of different numbers of inputs and of various weights belonging to the intervals with varying sizes. The standard deviation of Gaussian distribution is selected depending on this interval size in order to compare networks with different weights intervals, which also constitutes a new approach for selecting the distribution’s parameters. The results of all analyzed networks are shown as a percentage of the synchronization time of a network with weights drawn from uniform distribution. The weights drawn from the Gaussian distribution with decreasing standard deviation have shorter synchronization time especially for a relatively small network

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On Application of Neural Networks for S-Boxes Design

Cited by 8 publications

References 5 publications

Cryptography and Cryptanalysis Through Computational Intelligence

Cryptography and Cryptanalysis Through Computational Intelligence

Novel Low-Power Construction of Chaotic S-Box in Multilayer Perceptron

The Impact of the TPM Weights Distribution on Network Synchronization Time

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