Model that Solve the Information Recovery Problems

• Autorzy: Kazakova N. , Skopa O. , Karpiński M.
• Języki publikacji: EN

Abstrakty

EN

The main part of the information ensuring in information and communication systems (ICS) is the provision for the development of methods for monitoring, optimization and forecasting facilities. Accordingly, an important issue of information security is a challenge to improve the monitoring systems accuracy. One way is to restore the information from the primary control sensors. Such sensors may be implemented in the form of technical devices, and as a hardware and software systems. This paper reviews and analyzes the information recovery models using data from monitoring systems that watch the state of information systems objects and highlights its advantages and disadvantages. The aim of proposed modeling is to improve the accuracy of monitoring systems.

Słowa kluczowe

EN

accuracy; information recovery; model; monitoring

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2014
• Tom: nr 4
• Strony: 116--121
• Opis fizyczny: Bibliogr. 24 poz.

Twórcy

autor

Kazakova N.

• Odessa National Economic University, Odessa, Ukraine

autor

Skopa O.

• Odessa National Economic University, Odessa, Ukraine

autor

Karpiński M.

• University of Bielsko-Biala, Bielsko-Biala, Poland

Bibliografia

• [1] G. Weikum and G. Vossen, Transactional Information Systems: Theory, Algorithms, and the Practice of Concurrency Control and Recovery. Morgan Kaufmann, 2001.
• [2] N. F. Kazakova and O. O. Skopa "Definition of parameters for solving predictive control multi-service telecommunication networks", Modern Inform. Secur., no. 4, Special Issue, pp. 55-61, 2010 (in Ukrainian).
• [3] V. S. Frost and B. Melamed "Traffic modeling for telecommunications networks", Commun. Mag., vol. 32, no. 3, pp. 70-81, 1994.
• [4] N. F. Kazakova, N. M. Bilyk, and G. A. Gunderich, "Fundamental problems of classiffication and analysis of models for software and predictive control of multi-telecommunication networks", Nat. Univ. Shipbuilding (NUS Journal), no. 2 [431], pp. 125-132, 2010 (in Russian).
• [5] J. Wu, J. Meng, and W. Liu, "Iterative solutions for a class of systems of abstract binary operator equations", in Proc. 5th Int. Conf. Comput. Inform. Sci. ICCIS 2013, Shiyan, China, 2013, pp. 888-890.
• [6] N. F. Kazakova, "Application software implemented predictive control in the sense of solving practical problems of quality assurance services in secure information networks", A Modern Special Technique, no. 2, pp. 86-95, 2012. (in Ukrainian).
• [7] C. G. Park, "On the stability of the linear mapping in Banach modules", J. Mathem. Anal. and Appl., vol. 275, no. 2, pp. 711-720, 2002.
• [8] E. J. Candès, J. Romberg, and T. Tao, "Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information", IEEE Trans. Inform. Theory., vol. 52, no. 2, pp. 489-509, 2006.
• [9] V. L. Baranov et al., Recovery and Optimization of Information Systems in Decision Making. Kiev: State University of Information and Communication Technologies, 2009 (in Ukrainian).
• [10] M. E. Kilmer and D. P. O'Leary, "Choosing regularization parameters in iterative methods for ill-posed problems", SIAM J. Matrix Anal. Appl., vol. 22, no. 4, pp. 1204-1221, 2001.
• [11] A. N. Tikhonov and V. Y. Arsenin, Solutions of Ill-Posed Problems. Washington, D.C.: Wiley, 1977.
• [12] J. Zhu, C. K. Zhong, and Y. J. Cho, "Generalized variational principle and vector optimization", J. Optimiz. Theory Appl., vol. 106, no. 1, pp. 201-217, 2000.
• [13] A. N. Voronin, Multicriteria Synthesis of Dynamic Systems. Kiev: Naukova Dumka, 1992 (in Russian).
• [14] B. P. Zeigler, H. Praehofer, and T. G. Kim, Theory of Modelling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems. Academic Press, 2000.
• [15] A. N. Voronin, J. K. Ziatdinov, A. I. Kozlov, and V. S. Chabanyuk, Vector Optimization of Dynamic Systems. Kiev: Technika, 1999 (in Russian).
• [16] K. Deb and H. Gupta, Searching for robust Pareto-optimal solutions in multi-objective optimization, Lecture Notes in Computer Science, vol. 3410, pp. 150-164, Springer, 2005.
• [17] V. V. Podinovskii and V. D. Nogin, Pareto-Optimal Solutions of Multiobjective Problems. Moscow: Nauka, 1982 (in Russian).
• [18] J. Haupt and R. Nowak. "Signal reconstruction from noisy random projections", Information Theory, IEEE Transactions on., 2006, vol. 52, no. 9, pp. 4036-4048.
• [19] V. L. Baranov, I. A. Zhukov, and A. A. Zasyadko, "Using the main criterion for solving the problem of signal restoration", Bull. National Aviation University, no. 1, pp. 9-13, 2003 (in Ukrainian).
• [20] A. A. Zasyadko, "Comparison of the Tikhonov method and the method of multicriteria optimization for solving the signal restoration problem", J. Autom. Inform. Sci., vol. 35, no. 9, pp. 45-52, 2003.
• [21] K. E. Parsopoulos and M. N. Vrahatis, "Particle swarm optimization method in multiobjective problems", in Proc. ACM Symposium on Applied Computing SAC 2002, Madrid, Spain, 2002, pp. 603-607.
• [22] A. A. Zasyadko, "Multiple objective restoration process signals", Electr. Simul., vol. 26, no. 4, pp. 13-21, 2004 (in Russian).
• [23] T. Lobos et al., "High-resolution spectrum-estimation methods for signal analysis in power systems", IEEE Trans. Instrum. Measur., vol. 55, no. 1, pp. 219-225, 2006.
• [24] S. M. Kay and S. L. Marple (Jr), "Spectrum analysis - A modern perspective", Proc. IEEE, vol. 69, no. 11, pp. 1380-1419. 1981.