Using Neural Networks to Create and Test Pseudorandom Number Generators
The article presents an overview of modern researches in the field of neural cryptography in relation to pseudorandom number generators (PRNG). Various types of PRNGs and their implementation are provided. We provide the criteria, due to which the PRNG can be considered cryptographically secure (CSP...
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| Format: | Article |
| Language: | English |
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Joint Stock Company "Experimental Scientific and Production Association SPELS
2023-12-01
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| Series: | Безопасность информационных технологий |
| Subjects: | |
| Online Access: | https://bit.spels.ru/index.php/bit/article/view/1550 |
| _version_ | 1797403194277494784 |
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| author | Alexey M. Bulygin Ilya V. Chugunkov |
| author_facet | Alexey M. Bulygin Ilya V. Chugunkov |
| author_sort | Alexey M. Bulygin |
| collection | DOAJ |
| description | The article presents an overview of modern researches in the field of neural cryptography in relation to pseudorandom number generators (PRNG). Various types of PRNGs and their implementation are provided. We provide the criteria, due to which the PRNG can be considered cryptographically secure (CSPRNG). There are reasons for using certain types of generators. We briefly describe the theory underlying neural networks (NN). We carry the comparison of the NN architectures in the application to the tasks of creating a PRNG and testing output sequences out. Various sets of statistical tests for the analysis of output sequences from PRNG are presented. We consider the results of the most significant articles on the creation of a PRNGs based on the NN. We study articles that based on both classical recurrent networks (Elman, LSTM) and modern generative-adversarial network (GAN). The study of the methods of testing the RNG with the help of NN is implemented. We consider methods of analyzing the output sequences of the RNG and the negative consequences of underestimating the importance of this stage. We describe trends in the neural cryptography, such as the study of numbers that were originally considered random (for example, the number π) and the analysis of the output sequences of quantum random number generators (QRNG) for the presence of patterns. |
| first_indexed | 2024-03-09T02:34:59Z |
| format | Article |
| id | doaj.art-f0f76dcc7a2b43fd922b0face7fc829c |
| institution | Directory Open Access Journal |
| issn | 2074-7128 2074-7136 |
| language | English |
| last_indexed | 2024-03-09T02:34:59Z |
| publishDate | 2023-12-01 |
| publisher | Joint Stock Company "Experimental Scientific and Production Association SPELS |
| record_format | Article |
| series | Безопасность информационных технологий |
| spelling | doaj.art-f0f76dcc7a2b43fd922b0face7fc829c2023-12-06T11:40:39ZengJoint Stock Company "Experimental Scientific and Production Association SPELSБезопасность информационных технологий2074-71282074-71362023-12-01304749110.26583/bit.2023.4.041345Using Neural Networks to Create and Test Pseudorandom Number GeneratorsAlexey M. Bulygin0Ilya V. Chugunkov1National Nuclear Research University MEPHI (Moscow Engineering Physics Institute)National Nuclear Research University MEPHI (Moscow Engineering Physics Institute)The article presents an overview of modern researches in the field of neural cryptography in relation to pseudorandom number generators (PRNG). Various types of PRNGs and their implementation are provided. We provide the criteria, due to which the PRNG can be considered cryptographically secure (CSPRNG). There are reasons for using certain types of generators. We briefly describe the theory underlying neural networks (NN). We carry the comparison of the NN architectures in the application to the tasks of creating a PRNG and testing output sequences out. Various sets of statistical tests for the analysis of output sequences from PRNG are presented. We consider the results of the most significant articles on the creation of a PRNGs based on the NN. We study articles that based on both classical recurrent networks (Elman, LSTM) and modern generative-adversarial network (GAN). The study of the methods of testing the RNG with the help of NN is implemented. We consider methods of analyzing the output sequences of the RNG and the negative consequences of underestimating the importance of this stage. We describe trends in the neural cryptography, such as the study of numbers that were originally considered random (for example, the number π) and the analysis of the output sequences of quantum random number generators (QRNG) for the presence of patterns.https://bit.spels.ru/index.php/bit/article/view/1550prng, neural cryptography, neural networks, prng testing, prng implementation, quantum rng. |
| spellingShingle | Alexey M. Bulygin Ilya V. Chugunkov Using Neural Networks to Create and Test Pseudorandom Number Generators Безопасность информационных технологий prng, neural cryptography, neural networks, prng testing, prng implementation, quantum rng. |
| title | Using Neural Networks to Create and Test Pseudorandom Number Generators |
| title_full | Using Neural Networks to Create and Test Pseudorandom Number Generators |
| title_fullStr | Using Neural Networks to Create and Test Pseudorandom Number Generators |
| title_full_unstemmed | Using Neural Networks to Create and Test Pseudorandom Number Generators |
| title_short | Using Neural Networks to Create and Test Pseudorandom Number Generators |
| title_sort | using neural networks to create and test pseudorandom number generators |
| topic | prng, neural cryptography, neural networks, prng testing, prng implementation, quantum rng. |
| url | https://bit.spels.ru/index.php/bit/article/view/1550 |
| work_keys_str_mv | AT alexeymbulygin usingneuralnetworkstocreateandtestpseudorandomnumbergenerators AT ilyavchugunkov usingneuralnetworkstocreateandtestpseudorandomnumbergenerators |