Design of chaotic neural network based method for cryptographic substitution box

Ahmad, Musheer; Malik, M. Ahmir

doi:10.1109/iceeot.2016.7754809

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…Other uses for Chaos NNs is pseudo random number generations using a peice wise linear chaotic map [165,166]. Besides using chaotic maps for pseudo random generation, it is also being used in research in conjunction with S-boxes in order to improve upon public key cryptography [167,168], while encryption via NNs may be possible, a comparison with AES shows it provides better performance at the cost of security for larger files [169]. -Genetic Algorithms: Genetic algorithms are heuristics based of the theory of evolution where the best performing individuals will be used to create the next generation of individuals for further optimization in the hopes to converge to an optimization.…”

Section: -Neural Physically Unclonable Function (Puf)mentioning

confidence: 99%

The Dichotomy of Neural Networks and Cryptography: War and Peace

Zolfaghari

Koshiba

2022

ASI

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In recent years, neural networks and cryptographic schemes have come together in war and peace; a cross-impact that forms a dichotomy deserving a comprehensive review study. Neural networks can be used against cryptosystems; they can play roles in cryptanalysis and attacks against encryption algorithms and encrypted data. This side of the dichotomy can be interpreted as a war declared by neural networks. On the other hand, neural networks and cryptographic algorithms can mutually support each other. Neural networks can help improve the performance and the security of cryptosystems, and encryption techniques can support the confidentiality of neural networks. The latter side of the dichotomy can be referred to as the peace. There are, to the best of our knowledge, no current surveys that take a comprehensive look at the many ways neural networks are currently interacting with cryptography. This survey aims to fill that niche by providing an overview on the state of the cross-impact between neural networks and cryptography systems. To this end, this paper will highlight the current areas where progress is being made as well as the aspects where there is room for future research to be conducted.

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Section: -Neural Physically Unclonable Function (Puf)mentioning

confidence: 99%

The Dichotomy of Neural Networks and Cryptography: War and Peace

Zolfaghari

Koshiba

2022

ASI

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“…The group G 5 =<x: x 32 =1> of order 32 with only one generator, that is, x:= (27,24,28,21,3,11,5,25,17,9,23,26,8,12,18,20,7,30,22,14,19,29,15,6,1,10,4,2,32,31,13,16); acts naturally on the index set I Ωi of A 1 that is, 5×5 S-box from chaos-based search. This action can be defined as:…”

Section: B Proposed Group Structures and Actionsmentioning

confidence: 99%

“…The successes of AES block cipher and its S-box is mainly responsible for the progress and development of proposals devoted to the creation of S-boxes [22]. All of them are balanced and whose designs are primarily based on concepts such as algebraic techniques, optimization, chaotic maps and systems, etc, [23][24][25][26][27][28][29][30]. Lambić proposed a simple search methodology for yielding S-boxes which is based on chaotic system and composition methods in [31].…”

Section: Introductionmentioning

confidence: 99%

Bijective S-Boxes Method Using Improved Chaotic Map-Based Heuristic Search and Algebraic Group Structures

et al. 2020

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This paper presents a hybrid method for the construction of cryptographically strong bijective substitution-boxes by utilizing the merits of chaotic map and algebraic groups. The hybrid method first generates the key-dependent dynamic S-boxes using chaotic heuristic search strategy and then the S-boxes are evolved with the help of potent proposed algebraic group structures. This paper proposes a new improved combination chaotic map to operate initial search strategy. To augment the strength of generated S-boxes, the algebraic group structures are discovered which have the power to improve their cryptographic strength. The performance assessments using standard criterions are rendered to quantify the strengths of proposed bijective S-boxes. The experimental results and comparisons with recent S-box research findings justify the effectiveness and competence of the proposed bijective S-boxes and anticipated hybrid generation method.

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“…A mathematical operation named as Determinant Rotation is introduced by [49] to produce different s-boxes for each round of AES. Ahmad and Malik [50] proposed the chaosbased neural network with four layers; input layer with eight neurons, two hidden layers with four neurons, two neurons respectively and output layer with one neuron. The sequence of the corresponding multiplicative inverse in GF(2 8 ) and artificial neural network with seven perceptron neurons in three layers operations are synthesized to generate an s-box [51].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Generation and Security Analysis of Substitution Box Using Fingerprint Patterns

2020

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Information and its security have attracted the research community in recent years with increasing usage of mobile applications. Mobile devices have different security options in data transmission such as reading some biometric values. The keystone of the modern block and stream ciphers is the use of a substitution box (s-box) that obscures correlation between plaintext and ciphertext. In this study, we proposed a novel s-box generation algorithm by using the fingerprint pattern of the person who transfers information to the target. We generated several s-boxes by using bifurcation and ridge ending features of the fingerprint. Proposed s-boxes are compared with several known s-boxes over nonlinearity, bijectiveness, strict avalanche criterion, bit independence criterion, linear probability, and differential probability. Along with these properties, we analyzed confidence interval and randomness properties of new s-boxes as well. Also, the execution time of the proposed s-box generation algorithm is calculated and examined. The results of the cryptographic properties have shown that the proposed s-boxes by using ridge ending of the fingerprint performs better. The performances analysis show that the proposed s-box has satisfactory results according to the results of chaotic-based s-boxes. On the other hand, the fingerprint sboxes are much better than the existing biometric s-boxes according to the s-box security metrics. The results have shown that the execution time of the proposed s-box generation algorithm is more minimum than the existing biometric s-box generation algorithms. Resulting from applying fingerprint biometric data to generate an s-box, such a successful algorithm is promising to be used in mobile devices. INDEX TERMS Biometric data, cryptography, fingerprint, mobile devices, substitution box.

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Design of chaotic neural network based method for cryptographic substitution box

Cited by 10 publications

References 18 publications

The Dichotomy of Neural Networks and Cryptography: War and Peace

The Dichotomy of Neural Networks and Cryptography: War and Peace

Bijective S-Boxes Method Using Improved Chaotic Map-Based Heuristic Search and Algebraic Group Structures

An Efficient Generation and Security Analysis of Substitution Box Using Fingerprint Patterns

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