OPTIMIZATION OF EQUIPMENT RESERVE FOR INTELLECTUAL AUTOMATED SYSTEMS

Authors

DOI:

https://doi.org/10.28925/2663-4023.2021.14.8799

Keywords:

Smart City; intelligent automated control system; equipment reserve; algorithm; optimization

Abstract

Algorithms for a neural network analyzer involved in the decision support system (DSS) during the selection of the composition of backup equipment (CBE) for intelligent automated control systems Smart City are proposed. A model, algorithms and software have been developed for solving the optimization problem of choosing a CBE capable of ensuring the uninterrupted operation of the IACS both in conditions of technological failures and in conditions of destructive interference in the operation of the IACS by the attackers. The proposed solutions help to reduce the cost of determining the optimal CBE for IACS by 15–17% in comparison with the results of known calculation methods. The results of computational experiments to study the degree of influence of the outputs of the neural network analyzer on the efficiency of the functioning of the CBE for IACS are presented.

Downloads

Download data is not yet available.

References

Alrashdi, I., Alqazzaz, A., Aloufi, E., Alharthi, R., Zohdy, M., & Ming, H. (2019, January). Ad-iot: Anomaly detection of iot cyberattacks in smart city using machine learning. In 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0305-0310). IEEE.

Rashid, M. M., Kamruzzaman, J., Hassan, M. M., Imam, T., & Gordon, S. (2020). Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques. International Journal of Environmental Research and Public Health, 17(24), 9347.

Lee, J., Kim, J., & Seo, J. (2019, January). Cyber attack scenarios on smart city and their ripple effects. In 2019 International Conference on Platform Technology and Service (PlatCon) (pp. 1-5). IEEE.

Kalinin, M., Krundyshev, V., & Zegzhda, P. (2021). Cybersecurity Risk Assessment in Smart City Infrastructures. Machines, 9(4), 78.

Kitchin, R., & Dodge, M. (2019). The (in) security of smart cities: Vulnerabilities, risks, mitigation, and prevention. Journal of Urban Technology, 26(2), 47-65.

Ferraz, F. S., & Ferraz, C. A. G. (2014, December). Smart city security issues: depicting information security issues in the role of an urban environment. In 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing (pp. 842-847). IEEE.

Wu, Y. C., Sun, R., & Wu, Y. J. (2020). Smart city development in Taiwan: From the perspective of the information security policy. Sustainability, 12(7), 2916.

Wang, D., Bai, B., Lei, K., Zhao, W., Yang, Y., & Han, Z. (2019). Enhancing information security via physical layer approaches in heterogeneous IoT with multiple access mobile edge computing in smart city. IEEE Access, 7, 54508-54521.

Asghar, M. R., Hu, Q., & Zeadally, S. (2019). Cybersecurity in industrial control systems: Issues, technologies, and challenges. Computer Networks, 165, 106946.

Hajian-Hoseinabadi, H. (2011). Impacts of automated control systems on substation reliability. IEEE Transactions on Power Delivery, 26(3), 1681-1691.

Weber, P., & Jouffe, L. (2006). Complex system reliability modelling with dynamic object oriented Bayesian networks (DOOBN). Reliability Engineering & System Safety, 91(2), 149-162.

Cai, B., Liu, H., & Xie, M. (2016). A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks. Mechanical Systems and Signal Processing, 80, 31-44.

Kuhn, R., & Culhane, D. P. (1998). Applying cluster analysis to test a typology of homelessness by pattern of shelter utilization: Results from the analysis of administrative data. American journal of community psychology, 26(2), 207-232.

Maździarz, A. Alarm Correlation in Mobile Telecommunications Networks based on k-means Cluster Analysis Method. Journal of telecommunications and information technology, 2, 2018, pp.95-102. https://doi.org/10.26636/jtit.2018.124518

Bapiyev, I. M., Aitchanov, B. H., Tereikovskyi, I. A., Tereikovska, L. A., & Korchenko, A. A. (2017). Deep neural networks in cyber attack detection systems. International Journal of Civil Engineering and Technology (IJCIET), 8(11), 1086-1092.

Wang, G., Hao, J., Ma, J., & Huang, L. (2010). A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering. Expert systems with applications, 37(9), 6225-6232.

Cilimkovic, M. (2015). Neural networks and back propagation algorithm. Institute of Technology Blanchardstown, Blanchardstown Road North Dublin, 15, 1-12.

Wilamowski, B. M. (2009). Neural network architectures and learning algorithms. IEEE Industrial Electronics Magazine, 3(4), 56-63.

Prechelt, L. (1996). A quantitative study of experimental evaluations of neural network learning algorithms: Current research practice. Neural Networks, 9(3), 457-462.

Karayiannis, N. B., & Venetsanopoulos, A. N. (1993). Fast learning algorithms for neural networks. In Artificial Neural Networks (pp. 141-193). Springer, Boston, MA.

Tamp, N. V., & Tamp, V. L. (2016). Programma raspoznavaniya sostoyanij informacionno-vychislitel'noj seti na osnove nejronnoj seti s obratnym rasprostraneniem oshibok. Svidetel'stvo o gosudarstvennoj registracii programmy dlya EVM Nomer svidetel'stva: RU 2016660599.

CHEN, Mu-Chen; HSU, Chih-Ming; CHEN, Shih-Wei. Optimizing joint maintenance and stock provisioning policy for a multi-echelon spare part logistics network. Journal of the Chinese Institute of Industrial Engineers, 2006, 23.4: 289-302.

MOURONTE-LÓPEZ, Mary Luz. Optimizing the spare parts management process in a communication network. Journal of Network and Systems Management, 2018, 26.1: 169-188.

Downloads


Abstract views: 370

Published

2021-12-30

How to Cite

Chubaievskyi, V., Lakhno, V. ., Akhmetov, B. ., Kryvoruchko, O., Kasatkin, D., Desiatko, A. ., & Litovchenko, T. . (2021). OPTIMIZATION OF EQUIPMENT RESERVE FOR INTELLECTUAL AUTOMATED SYSTEMS. Electronic Professional Scientific Journal «Cybersecurity: Education, Science, Technique», 2(14), 87–99. https://doi.org/10.28925/2663-4023.2021.14.8799

Most read articles by the same author(s)

1 2 3 > >>