The Improvement of Public Key Cryptography Based on Chaotic Neural Networks

Zhang, Yunpeng; Xue, Tongtong; Zhai, Zhengjun; Ma, C.; Cai, Xiaobin

doi:10.1109/isda.2008.267

Cited by 5 publications

(3 citation statements)

References 3 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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“…During the BP (for the decoder-stage only), the same data sample is delivered also to the decoder as desired target ''p i '' as step (5) of same algorithm 3. According to the instant-ML of the learning ratios (that introduced in introduction part), each desired target intersects with its corresponding neuron function-curve to produce new ''Pre activation new '' for each neuron ''i'' of the outputlayer ''op'' as formula (8 and 9).…”

Section: Encryptor-unit Of the Lite-ihncmentioning

confidence: 99%

“…In the past two decades, many researchers already have collected and combined different NNs with various classical cryptographic paradigms [4]. The designers invented many complex neurocryptography rules [5][6][7]. They have created their CA by using different learning algorithms to generate strong keys [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Instant-Hybrid Neural-Cryptography (IHNC) based on fast machine learning

Badr

2022

Neural Comput & Applic

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Nowadays, cryptographic systems’ designers are facing significant challenges in their designs. They have to constantly search for new ideas of fast unbreakable algorithms with a very powerful key generator. In this paper, we propose a novel hybrid neural-cryptography methodology. It depends on new rule of very fast Backpropagation (BP) instant machine learning (ML). This proposed Hybrid Cryptography system is constructed from Encryptor and Decryptor based on the asymmetric Autoencoder type. The Encryptor encrypts and compresses a set of data to be instant code (i-code) using public key. While the Decryptor recovers this i-code (ciphered-data) based on two keys together. The first is the private key and the other is called instant-key (i-key). This i-key is generated from 3 factors as well (the original data itself, the generated i-code and the private key). The i-key is changing periodically with every transformation of plain data set, so it is powerful unpredictable key against the brute force.

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