Possibilities of immune intelligent systems application for information system on railway
Автор: Lykova M.P., Volegzhanina I.S.
Журнал: Международный журнал гуманитарных и естественных наук @intjournal
Рубрика: Технические науки
Статья в выпуске: 11-4 (86), 2023 года.
Бесплатный доступ
The paper considers the concept of "intelligent immune algorithms" in the context of rail transport data processing. The article examines the data flow involved in the processing of transit railcars with processing at the marshalling yard. Typical business processes are described. Errors encountered when interpreting the data by typical business processes are classified. The principles of immune intelligent systems functioning are described. The possibility of using such systems for data flow scanning in the ES of JSC "Russian Railways" was considered. When writing the work, such methods as collection and analysis of information, data processing were used. The main conclusion of the work is that the intelligent immune systems are a good way for the development of the infrastructure of the railway transport in the future. The results of scientific research presumably can be used in JSC "Russian Railways" for the design of new information systems.
Immune algorithms, immune intelligent systems, data protection, information security, intrusion detection
Короткий адрес: https://sciup.org/170202005
IDR: 170202005 | DOI: 10.24412/2500-1000-2023-11-4-78-82
Список литературы Possibilities of immune intelligent systems application for information system on railway
- De Castro L.N., Timmis J. (2002) Artificial Immune Systems: A New Computational Intelligence Approach. Springer-Verlag. ISBN: 978-1-85233-594-6.
- Kephart JO (1994). «A biologically inspired immune system for computers». Proceedings of Artificial Life IV: The Fourth International Workshop on the Synthesis and Simulation of Living Systems. MIT Press. https://doi.org/10.7551/mitpress/1428.003.0017.
- Kim J.J., Bentley P., Aickelin U., et. al. Immune System Approaches to Intrusion Detection - A Review. Natural Computing. - 2008. - №6 (4). https://doi.org/10.1007/s11047-006-9026-4.
- Beck G., Habicht G.S. Immunity and the invertebrates // Scientific American. - 1996. -№ 275 (5). https://doi.org/10.1038/scientificamerican1196-60.
- Medzhitov R (2007) Recognition of microorganisms and activation of the immune response. Nature 449:7164. https://doi.org/10.1038/nature06246.
- Comeau K., Paradis P., Schiffrin E.L. Human and murine memory yS T cells: Evidence for acquired immune memory in bacterial and viral infections and autoimmunity. Cellular Immunology. - 2020. - № 357. - P. 104217. https://doi.org/10.1016/j.cellimm.2020.104217.
- Kanwal S., Hussain A., Huang K. Novel Artificial Immune Networks-based optimization of shallow machine learning (ML) classifiers. Expert Systems with Applications. - 2021. -№ 165 (1). - P. 113834. https://doi.org/10.1016Zj.eswa.2020.113834.
- Krens E.A., Molendijk L., Wullems G.I., Schilperoort R.A. (1982) In vitro Transformation of Plant Protoplasts with Ti-Plasmid DNA. Nature 296.
- Shoham Y., Leyton-Brown K. Algorithmic, Game-Theoretic, and Logical Foundations. -London: Cambridge University Press, 2008. - 519 p.
- Wooldridge M. An Introduction to MultiAgent Systems. - Chichester, John Wiley & Sons Ltd, 2002. - 461 p.
- Hewitt C., Inman J. (1991) DAI Betwixt and Between. «Intelligent Agents» to Open Systems Science IEEE Transactions on Systems, Man, and Cybernetics. https://doi.org/10.1109/21.135685.
- Zuccolotto M., Pereira C.E., Fasanotti L. et. al. Designing an Artificial Immune Systems for Intelligent Maintenance Systems // IFAC-PapersOnLine. - 2015. - №48 (3). - P. 1451-1456. https://doi.org/ 10.1016/j.ifacol.2015.06.291.
