https://oldena.lpnu.ua/handle/ntb/52541| Title: | Neural-Like Means for Data Streams Encryption and Decryption in Real Time |
| Authors: | Tsmots, Ivan Skorokhoda, Oleksa Tsymbal, Yurii Tesluyk, Taras Khavalko, Viktor |
| Affiliation: | Lviv Polytechnic National University |
| Bibliographic description (Ukraine): | Neural-Like Means for Data Streams Encryption and Decryption in Real Time / Ivan Tsmots, Oleksa Skorokhoda, Yurii Tsymbal, Taras Tesluyk, Viktor Khavalko // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 438–443. — (Hybrid Systems of Computational Intelligence). |
| Bibliographic description (International): | Neural-Like Means for Data Streams Encryption and Decryption in Real Time / Ivan Tsmots, Oleksa Skorokhoda, Yurii Tsymbal, Taras Tesluyk, Viktor Khavalko // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 438–443. — (Hybrid Systems of Computational Intelligence). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | intensive data stream neural networks geometric transformations model |
| Number of pages: | 6 |
| Page range: | 438-443 |
| Start page: | 438 |
| End page: | 443 |
| Abstract: | The “model of successive geometric transformations” paradigm has been adapted for the implementation of parallel-streaming neural network encryption-decryption of data in real time. A model and structure of a parallel-streaming neural-like element for the mode have been developed. |
| URI: | https://ena.lpnu.ua/handle/ntb/52541 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| URL for reference material: | http://www.cs.utsa.edu/~bylander/cs4793/learnsc32.pdf |
| References (Ukraine): | [1] A. V. Palagin, and Yu. S. Yakovlev, System integration of computer equipment. Vinnytsia: UNIVERSUM-Vinnytsia, 2005. (in Russian) [2] V. P. Gribachev, “Element base of hardware implementations of neural networks,” in Components and technologies, no. 8, 2006. (in Russian) [3] S. Haykin, Neural networks and learning machines, 3rd ed. New York: Prentice Hall, 2009. [4] Ye. V. Bodyanskiy and O. G. Rudenko, Artificial neural networks: architectures, learning, applications. Kharkiv: TELETEH, 2004. (in Russian) [5] W. S. McCulloch, and W. Pitts, “A logical calculus of the ideas immanent in nervous activity” in The Bulletin of Mathematical Biophysics, vol. 5, iss. 4, pp. 115–133, 1943. [6] ADALINE (Adaptive linear) [Electronic Resource]: http://www.cs.utsa.edu/~bylander/cs4793/learnsc32.pdf [7] K. Fukushima, “Cognitron: A self-organizing multilayered neural network” in Biological cybernetics, vol. 20, iss. 3-4, pp. 121–136, 1975. [8] J. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities” in Proceedings of the national academy of sciences, vol. 79, iss. 8, pp. 2554–2558, 1982. [9] J. Cao, and J. Liang, “Boundedness and stability for Cohen–Grossberg neural network with time-varying delays,” in Journal of Mathematical Analysis and Applications, vol. 296, iss. 2, pp. 665–685, 2004. [10] Yu. M. Rashkevich, R. O. Tkachenko, I. G. Tsmots, and D. D. Peleshko, Non-linear methods, algorithms and structures for processing of signals and images in real time: monograph. Lviv: Lviv Polytechnic Publishing House, 2014. (in Ukrainian) [11] I. G. Tsmots, O. V. Skorokhoda, and B. I. Balych, “Model and VLSI structures of the parallel-vertical type formal neuron using bus multiplexing,” in Modeling and Information Technologies, Digest of Scientific Papers of the G.E. Puhov Institute of Modeling Problems in the Energy, Lviv, vol. 67, pp. 160-166, 2013. (in Ukrainian) [12] I. G. Tsmots, O. V. Skorokhoda, and V. B. Krasovskii, “Models and VLSI structures of a parallel-vertical type formal neuron combining the processes of data incoming and processing,” in Modeling and Information Technologies, Digest of Scientific Papers of the G.E. Puhov Institute of Modeling Problems in the Energy, Lviv, vol. 70, pp. 137-145, 2013. (in Ukrainian) [13] I. G. Tsmots, O. V. Skorokhoda, and B. I. Balych, “Model and VLSI structure of a parallel-vertical type formal neuron with tabular macropartial results,” in Modeling and Information Technologies, Digest of Scientific Papers of the G.E. Puhov Institute of Modeling Problems in the Energy, Lviv, vol. 73, pp. 133-138, 2014. (in Ukrainian) [14] I. G. Tsmots, O. V. Skorokhoda, and V. M. Tesliuk, A device for calculating scalar product. Patent № 101922 Ukraine, G06F 7/38. Bul. no. 9, 2013. (in Ukrainian) [15] I. Tsmots, O. Skorokhoda, V. Rabyk, and I. Ignatyev, “Basic verticalparallel real time neural network components,” XIIth International Scientific and Technical Conference “Computer Sciences and Information Technologies” (CSIT), Lviv, pp. 344–347, 2017. [16] I. Izonin, R. Tkachenko, D. Peleshko, T. Rak, and D. Batyuk, “Learningbased image super-resolution using weight coefficients of synaptic connections,” Xth International Scientific and Technical Conference “Computer Sciences and Information Technologies” (CSIT), Lviv, pp. 25-29, 2015. [17] Y. Tsymbal, and R. Tkachenko, “A digital watermarking scheme based on autoassociative neural networks of the geometric transformations model,”2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP), Lviv, pp. 231-234, 2016. [18] M. Nazarkevych, R. Oliiarnyk, H. Nazarkevych, O. Kramarenko, and I. Onyshschenko, “The method of encryption based on Ateb-functions,” 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP), Lviv, pp. 129-133, 2016. [19] I Dronyuk., M. Nazarkevych, and Z. Poplavska, “Gabor filters generalization based on ateb-functions for information security,” in Advances in Intelligent Systems and Computing, vol. 659, pp. 195-206, 2018. |
| References (International): | [1] A. V. Palagin, and Yu. S. Yakovlev, System integration of computer equipment. Vinnytsia: UNIVERSUM-Vinnytsia, 2005. (in Russian) [2] V. P. Gribachev, "Element base of hardware implementations of neural networks," in Components and technologies, no. 8, 2006. (in Russian) [3] S. Haykin, Neural networks and learning machines, 3rd ed. New York: Prentice Hall, 2009. [4] Ye. V. Bodyanskiy and O. G. Rudenko, Artificial neural networks: architectures, learning, applications. Kharkiv: TELETEH, 2004. (in Russian) [5] W. S. McCulloch, and W. Pitts, "A logical calculus of the ideas immanent in nervous activity" in The Bulletin of Mathematical Biophysics, vol. 5, iss. 4, pp. 115–133, 1943. [6] ADALINE (Adaptive linear) [Electronic Resource]: http://www.cs.utsa.edu/~bylander/cs4793/learnsc32.pdf [7] K. Fukushima, "Cognitron: A self-organizing multilayered neural network" in Biological cybernetics, vol. 20, iss. 3-4, pp. 121–136, 1975. [8] J. J. Hopfield, "Neural networks and physical systems with emergent collective computational abilities" in Proceedings of the national academy of sciences, vol. 79, iss. 8, pp. 2554–2558, 1982. [9] J. Cao, and J. Liang, "Boundedness and stability for Cohen–Grossberg neural network with time-varying delays," in Journal of Mathematical Analysis and Applications, vol. 296, iss. 2, pp. 665–685, 2004. [10] Yu. M. Rashkevich, R. O. Tkachenko, I. G. Tsmots, and D. D. Peleshko, Non-linear methods, algorithms and structures for processing of signals and images in real time: monograph. Lviv: Lviv Polytechnic Publishing House, 2014. (in Ukrainian) [11] I. G. Tsmots, O. V. Skorokhoda, and B. I. Balych, "Model and VLSI structures of the parallel-vertical type formal neuron using bus multiplexing," in Modeling and Information Technologies, Digest of Scientific Papers of the G.E. Puhov Institute of Modeling Problems in the Energy, Lviv, vol. 67, pp. 160-166, 2013. (in Ukrainian) [12] I. G. Tsmots, O. V. Skorokhoda, and V. B. Krasovskii, "Models and VLSI structures of a parallel-vertical type formal neuron combining the processes of data incoming and processing," in Modeling and Information Technologies, Digest of Scientific Papers of the G.E. Puhov Institute of Modeling Problems in the Energy, Lviv, vol. 70, pp. 137-145, 2013. (in Ukrainian) [13] I. G. Tsmots, O. V. Skorokhoda, and B. I. Balych, "Model and VLSI structure of a parallel-vertical type formal neuron with tabular macropartial results," in Modeling and Information Technologies, Digest of Scientific Papers of the G.E. Puhov Institute of Modeling Problems in the Energy, Lviv, vol. 73, pp. 133-138, 2014. (in Ukrainian) [14] I. G. Tsmots, O. V. Skorokhoda, and V. M. Tesliuk, A device for calculating scalar product. Patent No 101922 Ukraine, G06F 7/38. Bul. no. 9, 2013. (in Ukrainian) [15] I. Tsmots, O. Skorokhoda, V. Rabyk, and I. Ignatyev, "Basic verticalparallel real time neural network components," XIIth International Scientific and Technical Conference "Computer Sciences and Information Technologies" (CSIT), Lviv, pp. 344–347, 2017. [16] I. Izonin, R. Tkachenko, D. Peleshko, T. Rak, and D. Batyuk, "Learningbased image super-resolution using weight coefficients of synaptic connections," Xth International Scientific and Technical Conference "Computer Sciences and Information Technologies" (CSIT), Lviv, pp. 25-29, 2015. [17] Y. Tsymbal, and R. Tkachenko, "A digital watermarking scheme based on autoassociative neural networks of the geometric transformations model,"2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP), Lviv, pp. 231-234, 2016. [18] M. Nazarkevych, R. Oliiarnyk, H. Nazarkevych, O. Kramarenko, and I. Onyshschenko, "The method of encryption based on Ateb-functions," 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP), Lviv, pp. 129-133, 2016. [19] I Dronyuk., M. Nazarkevych, and Z. Poplavska, "Gabor filters generalization based on ateb-functions for information security," in Advances in Intelligent Systems and Computing, vol. 659, pp. 195-206, 2018. |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2018_Tsmots_I-Neural_Like_Means_for_Data_438-443.pdf | 375.68 kB | Adobe PDF | View/Open | |
| 2018_Tsmots_I-Neural_Like_Means_for_Data_438-443__COVER.png | 541 kB | image/png | View/Open |
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