Архітектура та реалізація базових компонентів системи нейромережевого захисту і кодування передачі даних

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dc.citation.epage62
dc.citation.issue1
dc.citation.journalTitleУкраїнський журнал інформаційних технологій
dc.citation.spage53
dc.citation.volume4
dc.contributor.affiliationНаціональний університет “Львівська політехніка”
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorЦмоць, І. Г.
dc.contributor.authorОпотяк, Ю. В.
dc.contributor.authorРізник, О. Я.
dc.contributor.authorБерезький, О. М.
dc.contributor.authorЛукащук, Ю. А.
dc.contributor.authorTsmots, I. G.
dc.contributor.authorOpotiak, Yu. V.
dc.contributor.authorRiznyk, O. Ya.
dc.contributor.authorBerezsky, O. M.
dc.contributor.authorLukashchuk, Yu. A.
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2024-03-20T09:41:09Z
dc.date.available2024-03-20T09:41:09Z
dc.date.created2022-02-28
dc.date.issued2022-02-28
dc.description.abstractОписано розробку базових компонентів системи нейромережевого захисту, кодування передачі даних на основі інтегрованого підходу, який містить удосконалений метод нейромережевого шифрування (дешифрування) даних і метод адаптивного баркероподібного кодування (декодування) даних, які орієнтовані на сучасну елементну базу. Для розробки системи обрано принципи спеціалізації та адаптації апаратно-програмних засобів до структури алгоритмів нейроподібного шифрування (дешифрування) даних, архітектури нейромережі та розрядності баркероподібного коду. Запропоновано архітектуру системи, що враховує змінний склад обладнання та модульність. Вдосконалено метод нейромережевого шифрування (дешифрування) даних, який внаслідок розпаралелення процесу шифрування (дешифрування) та використання таблиць макрочасткових добутків забезпечує зменшення часу шифрування (дешифрування) при програмній реалізації. Розроблено метод адаптивного баркероподібного кодування / декодування, який внаслідок врахування співвідношення сигнал/шум забезпечує високу завадостійкість та зменшує час передачі даних. Описано апаратні засоби системи, яку створено з використанням розроблених базових компонентів нейромережевого захисту та баркероподібного кодування даних. З використанням створеної системи визначено, що виконання операцій нейромережевого криптографічного шифрування (дешифрування) блоків даних на базі мікрокомп'ютера здійснюється у часі, близькому до реального. Час формування і навчання нейромережі становить біля 200 мс, а виконання процедур шифрування та дешифрування становить відповідно біля 35 мс та 30 мс і не залежить істотно від обраної конфігурації нейроподібної мережі.
dc.description.abstractThe development of basic components of the neural network protection system, data transmission coding based on an integrated approach, which includes an improved method of neural network encryption (decryption) and the method of adaptive barkerlike coding (decoding) of data, which focuses on modern element base. The principles of specialization and adaptation of hardware and software to the structure of algorithms for neuro-like encryption (decryption) of data, neural network architecture, and barker-like code are used to develop the system. The architecture of the system is proposed, which takes into account the variable composition of the equipment and modularity. The method of neural network encryption (decryption) of data has been improved. The time of neural network encryption and decryption of data depends on the size of the tables of macroparticle products. The size of the tables of pre-calculated macroparticle products is based on the provision of encryption and decryption of data in real-time. A method of adaptive barker-like encoding (decoding) has been developed, which, due to the signal-to-noise ratio, provides high noise immunity and reduces data transmission time. The hardware of the system, which was created using the developed basic components of neural network protection and barker-like data encoding, is described. When creating hardware, ready-made components and modules of industrial production are used as much as possible, and the availability of appropriate means of software code development is taken into account. Means of neural network cryptographic encryption (decryption) of data of the mobile part of the system are implemented using a microcomputer-based on SoC. Not the most powerful microcomputer of the NanoPi Duo type from FriendlyElec has been especially used to test the means of neural network cryptographic encryption (decryption) of data. Using the created system, it is determined that the performance of neural network cryptographic encryption (decryption) of data blocks based on a microcomputer is carried out in close to real-time. The time of formation and training of the neural network is about 200 ms, and the implementation of encryption and decryption procedures is about 35 ms and 30 ms, respectively, and does not depend significantly on the chosen configuration of the neural network.
dc.format.extent53-62
dc.format.pages10
dc.identifier.citationАрхітектура та реалізація базових компонентів системи нейромережевого захисту і кодування передачі даних / І. Г. Цмоць, Ю. В. Опотяк, О. Я. Різник, О. М. Березький, Ю. А. Лукащук // Український журнал інформаційних технологій. — Львів : Видавництво Львівської політехніки, 2022. — Том 4. — № 1. — С. 53–62.
dc.identifier.citationenArchitecture and implementation of basic components of neural network protection system and data transmission coding / I. G. Tsmots, Yu. V. Opotiak, O. Ya. Riznyk, O. M. Berezsky, Yu. A. Lukashchuk // Ukrainian Journal of Information Technology. — Lviv : Lviv Politechnic Publishing House, 2022. — Vol 4. — No 1. — P. 53–62.
dc.identifier.doidoi.org/10.23939/ujit2022.01.053
dc.identifier.issn2707-1898
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/61522
dc.language.isouk
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofУкраїнський журнал інформаційних технологій, 1 (4), 2022
dc.relation.ispartofUkrainian Journal of Information Technology, 1 (4), 2022
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dc.relation.references[7] Khan, S., Han, L., Lu, H., Butt, K., Bachira, G., & Khan, N. (2019). A New Hybrid Image Encryption Algorithm Based on 2D-CA, FSM-DNA Rule Generator, and FSBI. IEEE Access 2019, 7, 81333–81350. https://doi.org/10.1109/ACCESS.2019.2920383
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dc.relation.references[9] Ostapov, S. (2013). Information security technologies. Kharkiv: KhNEU.
dc.relation.references[10] Riznik, O. Ia., Tkachenko, R. O., & Kinash, Iu. Ye. (2019). Neiromerezheva tekhnologiia zakhistu ta peredachi danikh u realnomu chasi z vikoristanniam shumopodibnikh kodiv. Innovatciini tekhnologii u rozvitku suchasnogo suspilstva: zbirnik tez dopovidei mizhnarodnoi naukovo-praktichnoi konferentcii (Lviv, 18–19 kvitnia 2019 r.), 19–23. [In Ukrainian]. https://doi.org/10.1007/s00092-019-2378-8
dc.relation.references[11] Rudenko, O., & Bodyansky, E. (2006). Artificial neural networks. Kharkiv: SMIT Company Ltd.
dc.relation.references[12] Sagar, V., & Kumar, K. (2014). A Symmetric Key Cryptographic Algorithm Using Counter Propagation Network (CPN). Proceedings of the 2014 ACM International Conference on Information and Communication Technology for Competitive Strategies. https://doi.org/10.1145/2677855.2677906
dc.relation.references[13] Shihab, K. A. (2006). Backpropagation neural network for computer network security. Journal of Computer Science, vol. 2, no. 9, 710–715.
dc.relation.references[14] Śledź, S., Ewertowski, M. W., & Piekarczyk, J. (2021). Applications of unmanned aerial vehicle (UAV) surveys and Structure from Motion photogrammetry in glacial and periglacial geomorphology. Geomorphology 2021, 378 p. https://doi.org/10.1016/j.geomorph.2021.107620
dc.relation.references[15] Tcimbal, Iu. V. (2018). Neiromerezhevii metod simetrichnogo shifruvannia danikh. Visnik Natcionalnogo universitetu "Lvivska politekhnika". Seriia: Informatciini sistemi ta merezhi, 901, 118–122. [In Ukrainian].
dc.relation.references[16] Tereykovsky, I. (2007). Neural networks in the means of protection of computer information. Polygraph Consulting.
dc.relation.references[17] Tkachenko, R., Tkachenko, P., Izonin, I., & Tsymbal, Y. (2018). Learning-based image scaling using neural-like structure of geometric transformation paradigm. Advances in Soft Computing and Machine Learning in Image Processing, Springer, 537–565. https://doi.org/10.1007/978-3-319-63754-9_25
dc.relation.references[18] Tsmots, I. G., Rabik, V. G., & Lukashhuk, Iu. A. (2021). Rozroblennia mobilnikh zasobiv neiropodibnogo kriptografichnogo shifruvannia ta deshifruvannia danikh u realnomu chasi. Visnik Natcionalnogo universitetu "Lvivska politekhnika". Seriia: Informatciini sistemi ta merezhi, 9, 84–95. [In Ukrainian]. https://doi.org/10.23939/sisn2021.09.084
dc.relation.references[19] Tsmots, I., Rabyk, V., Riznyk, O., & Kynash, Y. (2019). Method of Synthesis and Practical Realization of Quasi-Barker Codes. 2019 IEEE 14th International Conference on Computer Sciences and Information Technologies (CSIT), Lviv, Ukraine, 76–79. https://doi.org/10.1109/STCCSIT.2019.8929882
dc.relation.references[20] Tsmots, I., Teslyuk, V., Teslyuk, T., Lukashchuk, Y. (2021). The method and simulation model of element base selection for protection system synthesis and data transmission. International Journal of Sensors, Wireless Communications and Control, 11(5), 518–530. https://doi.org/10.2174/2210327910999201022194630
dc.relation.references[21] Tsmots, I., Tsymbal, Y., Khavalko, V., Skorokhoda, O., & Tesluyk, T. (2018). Neural-Like Means for Data Streams Encryption and Decryption in Real Time. Processing of the 2018 IEEE 2nd International Conference on Data Stream Mining and Processing, DSMP 2018, 438–443. https://doi.org/10.1109/DSMP.2018.8478513
dc.relation.references[22] Tsmots, I., Tsymbal, Yu., Skorokhoda, O., & Tkachenko, R. (2019). Neural-like Methods and Hardware Structures for Real-time Data Encryption and Decryption. Proceedings of 14th International Scientific and Technical Conference (CSIT), Lviv, Ukraine, 3. 248–253. https://doi.org/10.1109/STCCSIT.2019.8929809
dc.relation.references[23] Tsymbal, Yu. (2018). Neural network method of symmetric data encryption. Bulletin of the Lviv Polytechnic National University. Information systems and networks, 901, 118–122.
dc.relation.references[24] Verma, A., & Ranga, V. (2020). Security of RPL based 6LoWPAN Networks in the Internet of Things: A Review. IEEE Sens. J., 20, 5666–5690. https://doi.org/10.1109/JSEN.2020.2973677
dc.relation.references[25] Volna, E., Kotyrba, M., Kocian, V., & Janosek, M. (2012). Cryptography Based On Neural Network. Proceedings of the 26th European Conference on Modeling and Simulation, 386–391. https://doi.org/10.7148/2012-0386-0391
dc.relation.references[26] Wang, M., Cong, S., & Zhang, S. (2018). Pseudo Chirp-Barker-Golay coded excitation in ultrasound imaging, 2018 Chinese Control And Decision Conference (CCDC), Shenyang, 4035–4039. https://doi.org/10.1109/CCDC.2018.8407824
dc.relation.references[27] Wang, S., & He, P. (2018). Research on Low Intercepting Radar Waveform Based on LFM and Barker Code Composite Modulation, 2018 International Conference on Sensor Networks and Signal Processing (SNSP), Xian, China, 297–301. https://doi.org/10.1109/SNSP.2018.00064
dc.relation.references[28] Zhou, K., Kang, Y., Huang, Y., & Feng, E. (2007). Encrypting Algorithm Based on RBF Neural Network. Proceedings of the IEEE Third International Conference on Natural Computation, 1, 765–768. https://doi.org/10.1109/ICNC.2007.353
dc.relation.referencesen[1] Arvandi, M., Wu, S., Sadeghian, A., Melek, W. W., & Woungang, I. (2006). Symmetric cipher design using recurrent neural networks. Proceedings of the IEEE International Joint Conference on Neural Networks, 2039–2046.
dc.relation.referencesen[2] Chang, A. X. M., Martini, B., & Culurciello, E. (2015). Recurrent neural networks hardware implementation on FPGA: arXiv preprint arXiv:1511.05552.
dc.relation.referencesen[3] Chi, Zhang, Wei, Zou, Liping, Ma, & Zhiqing, Wang. (2020). Biologically inspired jumping robots: A comprehensive review, Robotics and Autonomous Systems, vol. 124. https://doi.org/10.1016/j.robot.2019.103362
dc.relation.referencesen[4] Corona-Bermúdez, E., Chimal-Eguía, J. C., & Téllez-Castillo, G. (2022). Cryptographic Services Based on Elementary and Chaotic Cellular Automata. Electronics, 11(4), 613. https://doi.org/10.3390/electronics11040613
dc.relation.referencesen[5] Diamantaras, K. I., & Kung, S. Y. (1996). Principal Component Neural Networks. Theory and Applications (Wiley, 1996), 270 p.
dc.relation.referencesen[6] Haikin, S. (2016). Neural networks: full course ( 2nd ed . add. and revised). (Trans. from English). Moscow: Williams.
dc.relation.referencesen[7] Khan, S., Han, L., Lu, H., Butt, K., Bachira, G., & Khan, N. (2019). A New Hybrid Image Encryption Algorithm Based on 2D-CA, FSM-DNA Rule Generator, and FSBI. IEEE Access 2019, 7, 81333–81350. https://doi.org/10.1109/ACCESS.2019.2920383
dc.relation.referencesen[8] Korchenko, O., Tereykovsky, I., & Biloshchytsky, A. (2016). Methodology of development of neural network means of information security of Internet-oriented information systems. "Nash Format".
dc.relation.referencesen[9] Ostapov, S. (2013). Information security technologies. Kharkiv: KhNEU.
dc.relation.referencesen[10] Riznik, O. Ia., Tkachenko, R. O., & Kinash, Iu. Ye. (2019). Neiromerezheva tekhnologiia zakhistu ta peredachi danikh u realnomu chasi z vikoristanniam shumopodibnikh kodiv. Innovatciini tekhnologii u rozvitku suchasnogo suspilstva: zbirnik tez dopovidei mizhnarodnoi naukovo-praktichnoi konferentcii (Lviv, 18–19 kvitnia 2019 r.), 19–23. [In Ukrainian]. https://doi.org/10.1007/s00092-019-2378-8
dc.relation.referencesen[11] Rudenko, O., & Bodyansky, E. (2006). Artificial neural networks. Kharkiv: SMIT Company Ltd.
dc.relation.referencesen[12] Sagar, V., & Kumar, K. (2014). A Symmetric Key Cryptographic Algorithm Using Counter Propagation Network (CPN). Proceedings of the 2014 ACM International Conference on Information and Communication Technology for Competitive Strategies. https://doi.org/10.1145/2677855.2677906
dc.relation.referencesen[13] Shihab, K. A. (2006). Backpropagation neural network for computer network security. Journal of Computer Science, vol. 2, no. 9, 710–715.
dc.relation.referencesen[14] Śledź, S., Ewertowski, M. W., & Piekarczyk, J. (2021). Applications of unmanned aerial vehicle (UAV) surveys and Structure from Motion photogrammetry in glacial and periglacial geomorphology. Geomorphology 2021, 378 p. https://doi.org/10.1016/j.geomorph.2021.107620
dc.relation.referencesen[15] Tcimbal, Iu. V. (2018). Neiromerezhevii metod simetrichnogo shifruvannia danikh. Visnik Natcionalnogo universitetu "Lvivska politekhnika". Seriia: Informatciini sistemi ta merezhi, 901, 118–122. [In Ukrainian].
dc.relation.referencesen[16] Tereykovsky, I. (2007). Neural networks in the means of protection of computer information. Polygraph Consulting.
dc.relation.referencesen[17] Tkachenko, R., Tkachenko, P., Izonin, I., & Tsymbal, Y. (2018). Learning-based image scaling using neural-like structure of geometric transformation paradigm. Advances in Soft Computing and Machine Learning in Image Processing, Springer, 537–565. https://doi.org/10.1007/978-3-319-63754-9_25
dc.relation.referencesen[18] Tsmots, I. G., Rabik, V. G., & Lukashhuk, Iu. A. (2021). Rozroblennia mobilnikh zasobiv neiropodibnogo kriptografichnogo shifruvannia ta deshifruvannia danikh u realnomu chasi. Visnik Natcionalnogo universitetu "Lvivska politekhnika". Seriia: Informatciini sistemi ta merezhi, 9, 84–95. [In Ukrainian]. https://doi.org/10.23939/sisn2021.09.084
dc.relation.referencesen[19] Tsmots, I., Rabyk, V., Riznyk, O., & Kynash, Y. (2019). Method of Synthesis and Practical Realization of Quasi-Barker Codes. 2019 IEEE 14th International Conference on Computer Sciences and Information Technologies (CSIT), Lviv, Ukraine, 76–79. https://doi.org/10.1109/STCCSIT.2019.8929882
dc.relation.referencesen[20] Tsmots, I., Teslyuk, V., Teslyuk, T., Lukashchuk, Y. (2021). The method and simulation model of element base selection for protection system synthesis and data transmission. International Journal of Sensors, Wireless Communications and Control, 11(5), 518–530. https://doi.org/10.2174/2210327910999201022194630
dc.relation.referencesen[21] Tsmots, I., Tsymbal, Y., Khavalko, V., Skorokhoda, O., & Tesluyk, T. (2018). Neural-Like Means for Data Streams Encryption and Decryption in Real Time. Processing of the 2018 IEEE 2nd International Conference on Data Stream Mining and Processing, DSMP 2018, 438–443. https://doi.org/10.1109/DSMP.2018.8478513
dc.relation.referencesen[22] Tsmots, I., Tsymbal, Yu., Skorokhoda, O., & Tkachenko, R. (2019). Neural-like Methods and Hardware Structures for Real-time Data Encryption and Decryption. Proceedings of 14th International Scientific and Technical Conference (CSIT), Lviv, Ukraine, 3. 248–253. https://doi.org/10.1109/STCCSIT.2019.8929809
dc.relation.referencesen[23] Tsymbal, Yu. (2018). Neural network method of symmetric data encryption. Bulletin of the Lviv Polytechnic National University. Information systems and networks, 901, 118–122.
dc.relation.referencesen[24] Verma, A., & Ranga, V. (2020). Security of RPL based 6LoWPAN Networks in the Internet of Things: A Review. IEEE Sens. J., 20, 5666–5690. https://doi.org/10.1109/JSEN.2020.2973677
dc.relation.referencesen[25] Volna, E., Kotyrba, M., Kocian, V., & Janosek, M. (2012). Cryptography Based On Neural Network. Proceedings of the 26th European Conference on Modeling and Simulation, 386–391. https://doi.org/10.7148/2012-0386-0391
dc.relation.referencesen[26] Wang, M., Cong, S., & Zhang, S. (2018). Pseudo Chirp-Barker-Golay coded excitation in ultrasound imaging, 2018 Chinese Control And Decision Conference (CCDC), Shenyang, 4035–4039. https://doi.org/10.1109/CCDC.2018.8407824
dc.relation.referencesen[27] Wang, S., & He, P. (2018). Research on Low Intercepting Radar Waveform Based on LFM and Barker Code Composite Modulation, 2018 International Conference on Sensor Networks and Signal Processing (SNSP), Xian, China, 297–301. https://doi.org/10.1109/SNSP.2018.00064
dc.relation.referencesen[28] Zhou, K., Kang, Y., Huang, Y., & Feng, E. (2007). Encrypting Algorithm Based on RBF Neural Network. Proceedings of the IEEE Third International Conference on Natural Computation, 1, 765–768. https://doi.org/10.1109/ICNC.2007.353
dc.relation.urihttps://doi.org/10.1016/j.robot.2019.103362
dc.relation.urihttps://doi.org/10.3390/electronics11040613
dc.relation.urihttps://doi.org/10.1109/ACCESS.2019.2920383
dc.relation.urihttps://doi.org/10.1007/s00092-019-2378-8
dc.relation.urihttps://doi.org/10.1145/2677855.2677906
dc.relation.urihttps://doi.org/10.1016/j.geomorph.2021.107620
dc.relation.urihttps://doi.org/10.1007/978-3-319-63754-9_25
dc.relation.urihttps://doi.org/10.23939/sisn2021.09.084
dc.relation.urihttps://doi.org/10.1109/STCCSIT.2019.8929882
dc.relation.urihttps://doi.org/10.2174/2210327910999201022194630
dc.relation.urihttps://doi.org/10.1109/DSMP.2018.8478513
dc.relation.urihttps://doi.org/10.1109/STCCSIT.2019.8929809
dc.relation.urihttps://doi.org/10.1109/JSEN.2020.2973677
dc.relation.urihttps://doi.org/10.7148/2012-0386-0391
dc.relation.urihttps://doi.org/10.1109/CCDC.2018.8407824
dc.relation.urihttps://doi.org/10.1109/SNSP.2018.00064
dc.relation.urihttps://doi.org/10.1109/ICNC.2007.353
dc.rights.holder© Національний університет “Львівська політехніка”, 2022
dc.subjectкриптографічний захист
dc.subjectархітектура мобільної системи
dc.subjectбортові засоби
dc.subjectметод нейромережевого шифрування (дешифрування) даних
dc.subjectметод адаптивного баркероподібного кодування / декодування
dc.subjectcryptographic protection
dc.subjectmobile system architecture
dc.subjectneural network encryption (decryption) method
dc.subjectadaptive barker-like encoding (decoding) method
dc.titleАрхітектура та реалізація базових компонентів системи нейромережевого захисту і кодування передачі даних
dc.title.alternativeArchitecture and implementation of basic components of neural network protection system and data transmission coding
dc.typeArticle

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Ukrainian Journal of Information Technology. – 2022. – Vol. 4, No. 1