IMPLEMENTING MACHINE LEARNING FOR ENHANCED CRITICAL INFRASTRUCTURE PROTECTION: A FRAMEWORK-CENTRIC APPROACH FOR LEGACY SYSTEMS
Abstrakt
W artykule przedstawiono kompleksowe ramy integracji sztucznej inteligencji (AI) z systemami bezpieczeństwa infrastruktury krytycznej, ze szczególnym uwzględnieniem pokonywania wyzwań związanych z integracją ze starszymi systemami. Szczegółowo opisano konieczność ustrukturyzowanego podejścia do wdrażania sztucznej inteligencji, uwzględniającego przeszkody techniczne, regulacyjne i operacyjne w celu zwiększenia bezpieczeństwa i konserwacji. W artykule przez pryzmat badania pilotażowego omówiono napotkane wyzwania związane z integracją, podkreślając rolę frameworku w ułatwianiu płynniejszego przejścia na infrastrukturę wzmocnioną sztuczną inteligencją. Praca ta kładzie podwaliny pod przyszłe postępy w ochronie infrastruktury krytycznej za pomocą sztucznej inteligencji.
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Al-Rubaie M., Chang J. M., Privacy-preserving machine learning: Threats and solutions, “IEEE Security & Privacy”, 2019 Vol. 17 Issue 2.
Athey S., The impact of machine learning on economics, The economics of artificial intelligence: An agenda, University of Chicago Press 2018, Chicago.
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Brynjolfsson E., Mitchell T., What can machine learning do? Workforce implications, “Science”, 2017 Vol. 358 Issue 6370.
Cao R., Iansiti M., Digital transformation, data architecture, and legacy systems, “Journal of Digital Economy”, 2022 Vol. 1 Issue 1.
Carvalho T. P., Soares F. A., Vita R., Francisco R. d. P., Basto J. P., Alcalá S. G., A systematic literature review of machine learning methods applied to predictive maintenance, “Computers & Industrial Engineering”, 2019 Vol. 137.
Çınar Z. M., Abdussalam Nuhu A., Zeeshan Q., Korhan O., Asmael M., Safaei B., Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0, “Sustainability”, 2020 Vol. 12 Issue 19.
Dick K., Russell L., Souley Dosso Y., Kwamena F., Green J. R., Deep learning for critical infrastructure resilience, “Journal of Infrastructure Systems”, 2019 Vol. 25 Issue 2.
Engelmann B., Schmitt A.-M., Theilacker L., Schmitt J., Implications from Legacy Device Environments on the Conceptional Design of Machine Learning Models in Manufacturing, “Journal of Manufacturing and Materials Processing” 2024 Vol. 8 Issue 1.
Gorenstein A., Kalech M., Predictive maintenance for critical infrastructure, “Expert Systems with Applications”, 2022 Vol. 210.
Gupta B. B., Gaurav A., Panigrahi P. K., Arya V., Analysis of artificial intelligence-based technologies and approaches on sustainable entrepreneurship, “Technological Forecasting and Social Change”, 2023 Vol. 186.
Hemme K., Critical infrastructure protection: Maintenance is national security, “Journal of Strategic Security”, 2015 Vol. 8 Issue 3.
Hurst W., Shone N., Critical infrastructure security: Cyber-threats, legacy systems and weakening segmentation, n: Management and Engineering of Critical Infrastructures, Elsevier 2024.
Hussain F., Hussain R., Hassan S. A., Hossain E., Machine learning in IoT security: Current solutions and future challenges, “IEEE Communications Surveys & Tutorials”, 2020 Vol. 22 Issue 3.
Jha S., Jha M., O'Brien L., Wells M., Integrating legacy system into big data solutions: Time to make the change, Asia-Pacific World Congress on Computer Science and Engineering 2014, IEEE 2014.
Krupitzer C., Wagenhals T., Züfle M., Lesch V., Schäfer D., Mozaffarin A., et al., A survey on predictive maintenance for industry 4.0, arXiv preprint arXiv:2002.08224 2020.
Moteff J. D., Copeland C., Fischer J. W., Congressional Research Service, Library of Congress, Washington 2003.
Sharifani K., Amini M., Machine Learning and Deep Learning: A Review of Methods and Applications, “World Information Technology and Engineering Journal”, 2023 Vol. 10 Issue 07.
Seacord R. C., Plakosh D., Lewis G. A., Modernizing legacy systems: software technologies, engineering processes, and business practices, Publisher: Addison-Wesley Professional 2003.
Selcuk S., Predictive maintenance, its implementation and latest trends, Proceedings of the Institution of Mechanical Engineers, Part B: “Journal of Engineering Manufacture”, 2017 Vol. 231 Issue 9.
Xue M., Yuan C., Wu H., Zhang Y., Liu W., Machine learning security: Threats, countermeasures, and evaluations, IEEE Access, 2020 Vol. 8.
Zhang W., Yang D., Wang H., Data-driven methods for predictive maintenance of industrial equipment: A survey, “EEE Systems Journal”, 2019 Vol. 13 Issue 3.
Zonta T., Da Costa C. A., da Rosa Righi R., de Lima M. J., da Trindade E. S., Li G. P., Predictive maintenance in the Industry 4.0: A systematic literature review, “Computers & Industrial Engineering”, 2020 Vol. 150.
Opublikowane
2024-06-06
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Copyright (c) 2024 Maciej Grunt, Piotr Potejko
Utwór dostępny jest na licencji Creative Commons Uznanie autorstwa – Na tych samych warunkach 4.0 Miedzynarodowe.
