Encryption of Text Messages Using Multilayer Neural Networks

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dc.citation.epage6
dc.citation.issue2
dc.citation.spage1
dc.contributor.affiliationIvan Franko National University of Lviv
dc.contributor.affiliationThe State Higher School of Technology and Economicsin in Jarosław
dc.contributor.authorБригілевич, Володимир
dc.contributor.authorПелипець, Назар
dc.contributor.authorРабик, Василь
dc.contributor.authorBrygilevych, Volodymyr
dc.contributor.authorPelypets, Nazar
dc.contributor.authorRabyk, Vasyl
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2021-03-29T10:31:55Z
dc.date.available2021-03-29T10:31:55Z
dc.date.created2020-02-24
dc.date.issued2020-02-24
dc.description.abstractРозглянуто алгоритм шифрування/ дешифрування текстових повідомлень з використанням MLNN, який складається з трьох кроків: навчання нейронної мережі на основі навчаючих пар, сформованих з базового набору символів, що зустрічаються в тексті; шифрування повідомлення з використанням ваг прихованих шарів; його дешифрування з використанням ваг вихідного шару. Сформовано необхідні умови для успішного шифрування/ дешифрування цим алгоритмом, підкреслено його обмеження. Описано архітектуру і алгоритм навчання MLNN. Приведено експериментальні дослідження з допомогою програми NeuralNet: навчання MLNN методами BP(Sequential), BP(Batch), Rprop, QuickProp; приклад шифрування/ дешифрування текстового повідомлення.
dc.description.abstractThe article considers an algorithm for encrypting / decrypting text messages using multilayer neural networks (MLNN). The algorithm involves three steps: training a neural network based on the training pairs formed from a basic set of characters found in the text; encryption of the message using the weight coefficients of the hidden layers; its decryption using the weight coefficients of the output layer. The conditions necessary for successful encryption / decryption with this algorithm are formed, its limitations are emphasized. The MLNN architecture and training algorithm are described. The results of experimental research done by using the NeuralNet program are given: training the MLNN employing the BP (Sequential), BP (Batch), Rprop, QuickProp methods; an example of encrypting / decrypting a text message.
dc.format.extent1-6
dc.format.pages6
dc.identifier.citationBrygilevych V. Encryption of Text Messages Using Multilayer Neural Networks / Volodymyr Brygilevych, Nazar Pelypets, Vasyl Rabyk // Computational Problems of Electrical Engineering. — Lviv : Lviv Politechnic Publishing House, 2020. — Vol 10. — No 2. — P. 1–6.
dc.identifier.citationenBrygilevych V. Encryption of Text Messages Using Multilayer Neural Networks / Volodymyr Brygilevych, Nazar Pelypets, Vasyl Rabyk // Computational Problems of Electrical Engineering. — Lviv : Lviv Politechnic Publishing House, 2020. — Vol 10. — No 2. — P. 1–6.
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/56262
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofComputational Problems of Electrical Engineering, 2 (10), 2020
dc.relation.references[1] B. Schneier, Applied cryptography: Protocols, Algorithms, Source Code in C, Triumf, p. 815, 2012.
dc.relation.references[2] E. Volna, M. Kotyrba, V. Kocian, and M. Janosek, “Cryptography Based On Neural Network” // in Proc. 26th European Conference on Modeling and Simulation, pp. 386–391, 2012.
dc.relation.references[3] V. Sagar and K. Kumar, “A Symmetric Key Cryptographic Algorithm Using Counter Propagation Network (CPN)”, in Proc. 2014 ACM International Conference on Information and Communication Technology for Competitive Strategies, vol. ISBN, no. 978-1-4503-3216-3, 2014.
dc.relation.references[4] K. Shihab, “A backpropagation neural network for computer network security”, Journal of Computer Science, vol. 2, no. 9, pp. 710–715, 2006.
dc.relation.references[5] Choi-Kuen Chan, Chi-Kwong Chan, L. P. Lee, L. M. Cheng, Encryption system based on neural network, Communications and Multimedia Security Issues of the New Century, Springer, pp. 117–122, 2001.
dc.relation.references[6] M. Arvandi, S. Wu, A. Sadeghian, W. W. Melek, and I. Woungang, “Symmetric cipher design using recurrent neural networks”, in Proc. IEEE International Joint Conference on Neural Networks, pp. 2039–2046, 2006.
dc.relation.references[7] V. Bihday, V. Brygilevych, Y. Hychka, N. Pelypets, V. Rabyk, “Recognition of Handwritten Images Using Multilayer Neural Networks IEEE 2019”, in Proc. 11th International Scientific and Practical Conference on Electronics and Information Technologies, ELIT 2019 – Proceedings.
dc.relation.references[8] Simon Haykin, Neural Networks: A Comprehensive Foundation 2nd edition, Prentice Hall, NJ, USA ©1998, 842p, ISBN:0132733501.
dc.relation.references[9] M. Riedmiller and H. Brawn, RPROP – a fast adaptive learning algorithms. Technacal Report // Karlsruhe: University Karlsruhe. 1992. http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=6A4F81B00868291D27499A6AADC6C330?doi=10.1.1.52.4576 &rep=rep1&type=pdf
dc.relation.references[10] S. E. Fahlman, “Faster Learning Variations on Backpropagation: An Empirical Study”, in Proc. 1988 Connectionist Models Summer School, pp. 38–51, 1988
dc.relation.referencesen[1] B. Schneier, Applied cryptography: Protocols, Algorithms, Source Code in C, Triumf, p. 815, 2012.
dc.relation.referencesen[2] E. Volna, M. Kotyrba, V. Kocian, and M. Janosek, "Cryptography Based On Neural Network", in Proc. 26th European Conference on Modeling and Simulation, pp. 386–391, 2012.
dc.relation.referencesen[3] V. Sagar and K. Kumar, "A Symmetric Key Cryptographic Algorithm Using Counter Propagation Network (CPN)", in Proc. 2014 ACM International Conference on Information and Communication Technology for Competitive Strategies, vol. ISBN, no. 978-1-4503-3216-3, 2014.
dc.relation.referencesen[4] K. Shihab, "A backpropagation neural network for computer network security", Journal of Computer Science, vol. 2, no. 9, pp. 710–715, 2006.
dc.relation.referencesen[5] Choi-Kuen Chan, Chi-Kwong Chan, L. P. Lee, L. M. Cheng, Encryption system based on neural network, Communications and Multimedia Security Issues of the New Century, Springer, pp. 117–122, 2001.
dc.relation.referencesen[6] M. Arvandi, S. Wu, A. Sadeghian, W. W. Melek, and I. Woungang, "Symmetric cipher design using recurrent neural networks", in Proc. IEEE International Joint Conference on Neural Networks, pp. 2039–2046, 2006.
dc.relation.referencesen[7] V. Bihday, V. Brygilevych, Y. Hychka, N. Pelypets, V. Rabyk, "Recognition of Handwritten Images Using Multilayer Neural Networks IEEE 2019", in Proc. 11th International Scientific and Practical Conference on Electronics and Information Technologies, ELIT 2019 – Proceedings.
dc.relation.referencesen[8] Simon Haykin, Neural Networks: A Comprehensive Foundation 2nd edition, Prentice Hall, NJ, USA ©1998, 842p, ISBN:0132733501.
dc.relation.referencesen[9] M. Riedmiller and H. Brawn, RPROP – a fast adaptive learning algorithms. Technacal Report, Karlsruhe: University Karlsruhe. 1992. http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=6A4F81B00868291D27499A6AADC6C330?doi=10.1.1.52.4576 &rep=rep1&type=pdf
dc.relation.referencesen[10] S. E. Fahlman, "Faster Learning Variations on Backpropagation: An Empirical Study", in Proc. 1988 Connectionist Models Summer School, pp. 38–51, 1988
dc.relation.urihttp://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=6A4F81B00868291D27499A6AADC6C330?doi=10.1.1.52.4576
dc.rights.holder© Національний університет “Львівська політехніка”, 2020
dc.subjectencryption
dc.subjectdecryption
dc.subjectmultilayer neural networks
dc.subjecttraining algorithms
dc.subjectNeuralNet program
dc.titleEncryption of Text Messages Using Multilayer Neural Networks
dc.title.alternativeШифрування текстових повідомлень з допомогою багатошарових нейронних мереж
dc.typeArticle

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