Advanced methods of application of unmanned technologies in the development of the inland water transport of the Russian Federation and methods of information processing for enhancing information security of navigation data transmission are described.
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Vasiliev, Y.S., Zegzhda, P.D., and Kuvshinov, V.I., Modern problems of cybersecurity, Nonlinear Phenom. Complex Syst. (Dordrecht, Neth.), 2014, vol. 17, no. 3, pp. 210–214.
Anisimov, V.G., Anisimov, E.G., Zegzhda, P.D., and Suprun, A.F., The problem of innovative development of information security systems in the transport sector, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 1105–1110.
Strategy for the Development of Inland Water Transport of the Russian Federation for the Period up to 2030. https://mintrans.ru.
Sikarev, I.A. and Garanin, A.V., General principles of constructing a motion control system for a remotely operated sea vessel in the port area based on the NMEA-2000 network protocol, Autom. Control Comput. Sci., 2019, vol. 53, no. 8, pp. 932–936.
Matveev, A.A., Kuznetsov, V.N., and Gaskarov, V.D., Review of technical means and methods of satellite navigation of ships, Vestn. Gos. Univ. Morsk. Rechn. Flota im. Admirala S. O. Makarova, 2016, no. 1.
Nyrkov, A.P. and Chistyakov, G.B., Navigation systems GLONASS/DGPS for navigation safety, Materialy mezhdunar. nauch.-praktich. konf., posvyashchennoi 200-letiyu podgotovki kadrov dlya vodnogo transporta Rossii “Vodnye puti Rossii: Stroitel’stvo, ekspluatatsiya, upravlenie” (Proc. Int. Sci.-Pract. Conference Dedicated to the 200th Anniversary of Training Personnel for Water Transport in Russia “Waterways of Russia: Construction, Operation, and Management”), St. Petersburg, 2009, pp. 89–92.
Karetnikov, V.V., Volkov, R.V., and Kiselevich, G.V., The use of the river differential subsystem GLONASS/GPS in the inland waterways of the Russian Federation during track works, Vestn. Gos. Univ. Morsk. Rechn. Flota im. Admirala S. O. Makarova, 2015, no. 3, pp. 63–68.
Meerovich, V.D. and Dolgii, I.D., Stochastic filtration of navigation parameters of mobile objects through the integration of satellite and tracker measurements, Izv. Vyssh. Uch. Zaved., Sev.-Kavk. Reg., Ser.: Tekh. Nauki, 2015, no. 1, pp. 19–26.
Pavlenko, E. and Zegzhda, D., Sustainability of cyber-physical systems in the context of targeted destructive influences, IEEE Industrial Cyber-Physical Systems, ICPS 2018, 2018, pp. 830–834.
Zegzhda, D.P., Poltavtseva, M.A., and Lavrova, D.S., Systematization and security assessment of cyber-physical systems, Autom. Control Comput. Sci., 2017, vol. 51, no. 8, pp. 835–843.
Krundyshev, V. and Kalinin, M., Prevention of false data injections in smart infrastructures, IEEE International Black Sea Conference on Communications and Networking, 2019. https://doi.org/10.1109/BlackSeaCom.2019.8812786
Dakhnovich, A.D., Moskvin, D.A., and Zegzhda, D.P., Analysis of the information security threats in the digital production networks, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 1071–1075.
Lavrova, D., Zegzhda, D., and Yarmak, A., Predicting cyber attacks on industrial systems using the Kalman filter, 3rd World Conference on Smart Trends in Systems, Security and Sustainability, WorldS4 2019, 2019, pp. 317–321.
Kalinin, M.O., Lavrova, D.S., and Yarmak, A.V., Detection of threats in cyberphysical systems based on deep learning methods using multidimensional time series, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 912–917.
Zegzhda, D., Lavrova, D., and Poltavtseva, M., Multifractal security analysis of cyberphysical systems, Nonlinear Phenom. Complex Syst. (Dordrecht, Neth.), 2019, vol. 22, no. 2, pp. 196–204.
The authors declare that they have no conflicts of interest.
Translated by O. Pismenov
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Sikarev, I.A., Chistyakov, G.B., Garanin, A.V. et al. Algorithms for Enhancing Information Security in the Processing of Navigation Data of Unmanned Vessels of the Technical Fleet of the Inland Waterways of the Russian Federation. Aut. Control Comp. Sci. 54, 964–967 (2020). https://doi.org/10.3103/S0146411620080325
- global navigation satellite system
- unmanned shipping
- automated vessel traffic control systems