Informatics and Applications

2026, Volume 20, Issue 2, pp 82-88

SEARCH FOR ANOMALIES IN NETWORK-CENTRIC CONTROL SYSTEMS

A. A. Grusho
N. A. Grusho
M. I. Zabezhailo
A. A. Zatsarinny
V. O. Piskovski

Abstract

The use of network-centric technologies in control and decision support systems is determined by the concept of development of control and monitoring of peripheral nodes of the network-centric system (SCS) and the conditions for ensuring information security. Security issues in SCS have a number of specific features. The main goal of the Center can be described as a controlling cause consisting of properties that ultimately generate actions throughout the system. The control cause consists of a plurality of separate properties which themselves serve as the causes for sets of properties of slave SCS nodes functioning simultaneously as derived causes. The paper discusses problems of monitoring anomalies in the SCS when performing the main task set by the Center for peripheral nodes. Three types of anomalies were identified: (i) distortion of the properties of the consequences of derivative causes that give rise to actions to implement the plans of the Center; (ii) presence of obstacles when performing actions by nodes in the interests of the Center; and (iii) increased resource consumption which may slow down or stop the execution of actions at the remote node in the interests of the Center. The greatest difficulties are associated with the identification of anomalies of the first type.

[+] References (28)

Roshchepiy, I. 04.01.2025. Setetsentricheskaya voyna: chemu nauchil sovetskiy marshal Ogarkov armiyu SShA [Network-centric warfare: What Marshal of the Soviet Union Ogarkov taught the U.S. Army]. Russkaya semerka [Russian Seven]. Available at: https://russian7.ru/post/setecentricheskaya-voyna-chemu-nauchil-s/; https://dzen.ru/a/Z3lduCbj0llJCzqY (accessed May 29, 2026).
Pospelov, G. S., and V.A. Irikov. 1976. Programmno- tselevoe planirovanie i upravlenie [Program and target planning and management]. Moscow: Sovetskoe radio [Soviet radio]. 440 p.
A guide to the project management body of knowledge (PMBOK® Guide). 2021. 7th ed. Newtown Square, PA: Project Management Institute. 370 p.
Grusho, A., E. Timonina, and S. Shorgin. 2017. Vulnerabilities of a network management system. Applied Problems in Theory of Probabilities and Mathematical Statistics into Telecommunicationsa: 11th Workshop (International) on Applied Problems in Theory of Probabilities and Mathematical Statistics into Telecommunications Proceedings. Eds. D. Araniti, K. E. Samuylov, and S. Ya. Shorgin. Moscow: RUDN. 9. EDN: ZVLOQR.
Grusho, A. A., N.A. Grusho, M. I. Zabezhailo, D. V Smirnov, E. E. Timonina, and S. Ya. Shorgin. 2022. O bezopasnoy arkhitekture vychislitel'noy sistemy na osnove mikroservisov [About the secure architecture of a microservice-based computing system]. Informati- ka i ee Primeneniya - Inform. Appl. 16(4):87-92. doi: 10.14357/19922264220413. EDN: SUBXMB.
Pearl, J. 1995. Causal diagrams for empirical research. Biometrika 82(4):669-688. doi: 10.2307/2337329.
Pearl, J. 1996. Structural and probabilistic causality. Psychol. Learn. Motiv. 34:393-435. doi: 10.1016/S0079- 7421(08)60566-6.
Balke, A., and J. Pearl. 1994. Probabilistic evaluation of counterfactual queries. 12th National Conference on Artificial Intelligence Proceedings. Menlo Park, CA: MIT Press. 1:230-237.
Mohan, K., J. Pearl., and J. Tian. 2013. Graphical models for inference with missing data. Adv. Neur. In. 26:12771285.
Pearl, J. 2019. The seven tools of causal inference, with reflections on machine learning. Commun. ACM 62(3):54- 60. doi: 10.1145/3241036.
Mohan, K., and J. Pearl. 2021. Graphical models for processing missing data. J. Am. Stat. Assoc. 116(534):1023- 1037. doi: 10.1080/01621459.2021.1874961.
Bareinboim, E., and J. Pearl. 2016. Causal inference and the data-fusion problem. P. Natl. Acad. Sci. USA 113(27):7345-7352. doi: 10.1073/pnas.1510507113.
Halpern, J. Y., and J. Pearl. 2005. Causes and explanations: A structural-model approach - Part I: Causes. Brit. J. Philos. Sci. 56(4):843-887.
VanderWeele, T. J. 2015. Explanation in causal inference: Methods for mediation and interaction. New York, NY: Oxford University Press. 706 p.
Huang, X., and J. Marques-Silva. 2023. The inadequacy of Shapley values for explainability. Cornell University. 26 p. Available at: https://arxiv.org/pdf/2302.08160 (accessed May 29, 2026).
Gurevich, G., D. Kliger, and B. Weiner. 2012. The role of attribution of causality in economic decision making. J. Socio-Econ. 41(4):439-444. doi: 10.1016/ j.socec.2011.07.005.
Wang, Y., D. Liang, L. Charlin, and D.M. Blei. 2020. Causal inference for recommender systems. 14th Conference on Recommender Systems Proceedings. New York, NY: ACM. 426-431. doi: 10.1145/3383313.3412225.
Gao, C., Yu Zheng, W Wang, F. Feng, X. He, and Y. Li. 2022. Causal inference in recommender systems: A survey and future directions. Cornell University. 32 p. Available at: https://arxiv.org/pdf/2208.12397 (accessed May 29, 2026).
Vivek, Y., V. Ravi, A. A. Mane, and L. R. Naidu. 2022. Explainable artificial intelligence and causal inference based ATM fraud detection. Cornell University. 34 p. Available at: https://arxiv.org/pdf/2211.10595 (accessed May 29, 2026).
Abel, S., Y. Tang, J. Singh, and P. Paek. 2020. Applications of causal modeling in cybersecurity: An exploratory approach. Advances Science Technology Engineering Systems J. 5(3):380-387. doi: 10.25046/aj050349.
Grusho, A.A., N.A. Grusho, M. I. Zabezhailo, A.A. Zatsarinny, E. E. Timonina, and S. Ya. Shorgin. 2022. Analiz tsepochek prichinno-sledstvennykh svyazey [Cause-and- effect chain analysis]. Informatika i ee Primeneniya - Inform. Appl. 16(2):68-74. doi: 10.14357/19922264220209. EDN: HPSLTU.
Weinberg, A. I., C. Premebida, and D. R. Faria. 2025. Causality from bottom to top: A survey. Mach. Learn. 114:234. 37 p. doi: 10.1007/s10994-025-06855-5.
Grusho, A. A., M. I. Zabezhailo, D.V. Smirnov, and E. E. Timonina. 2017. Model' mnozhestva informatsionnykh prostranstv v zadache poiska insaydera [The model of the set of information spaces in the problem of insider detection]. Informatika i ee Primeneniya - Inform. Appl. 11(4):65-69.doi: 10.14357/19922264170408. EDN: ZXWUOP
Grusho, A.A., N.A. Grusho, M.I. Zabezhailo, V. V. Kulchenkov, E. E. Timonina, and S.Ya. Shorgin. 2023. Prichinno-sledstvennye svyazi v zadachakh klassifikatsii [Causal relationships in classification problems]. Informatika i ee Primeneniya - Inform. Appl. 17(1):43-49. doi: 10.14357/19922264230106. EDN: DTQZPK.
Grusho, A.A., N.A. Grusho, M.I. Zabezhailo, A. A. Zatsarinny, E. E. Timonina, and S.Ya. Shorgin. 2023. Zadacha klassifikatsii v usloviyakh iskazhennykh prichinno-sledstvennykh svyazey [The classification problem in conditions of distorted cause-and-effect relationships]. Sistemy i Sredstva Informatiki - Systems and Means of Informatics 33(1):59-67. doi: 10.14357/ 08696527230106. EDN: CJKRRR.
Grusho, A. A., N. A. Grusho, M. I. Zabezhailo, V. V. Kulchenkov, and E. E. Timonina. 2025. Kosvennye priznaki v zadachakh klassifikatsii dannykh bol'shoy razmernosti s pomoshch'yu prichinno-sledstvennykh svyazey [Indirect properties in classification of large data with the help of cause-and-effect relationships]. Informatika i ee Primeneniya - Inform. Appl. 19(4):72-77. doi: 10.14357/19922264250408. EDN: UZKQSR.
Grusho, A.A., N. A. Grusho, M.I. Zabezhailo, and E. E. Timonina. 2021. Udalennyy monitoring rabochikh protsessov [Remote monitoring of workflows]. Informatika i ee Primeneniya - Inform. Appl. 15(3):2-8. doi: 10.14357/19922264210301. EDN: VWANOS.
Grusho, A. A., N. A. Grusho, M. I. Zabezhailo, and E. E. Timonina. 2020. Lokalizatsiya iskhodnoy prichiny anomalii [Root cause anomaly localization]. Problemy informatsionnoy bezopasnosti. Komp'yuternye sistemy [Problems of Information Security. Computer Systems] 4:9-16. EDN: JXHQTY.

[+] About this article