A METHOD OF DISTRIBUTED MONITORING OF TELECOMMUNICATION NETWORKS BASED ON AN AGENT-BASED APPROACH

DOI: 10.31673/2786-8362.2024.022709

Abstract

The purpose of the article is to develop a method of distributed monitoring of
information and telecommunication networks (ITN) based on an agent-based approach and implementing
a step-by-step detection of abnormal situations in networks. The article deals with the urgent problem of
ensuring reliability and functional security of elements of modern network infrastructures. Particular
attention is paid to heterogeneous general and special-purpose networks, which are characterised by
heterogeneity in the use of various network technologies, information security tools and software and
hardware solutions.
The proposed approach is based on the principles of decentralisation and multi-level monitoring. The
solution is aimed at overcoming the difficulties arising from the distribution of network resources,
variability of network traffic characteristics and heterogeneity of ITCM elements. The article focuses on
the need to create monitoring subsystems that can take into account the heterogeneity and diversity of
network elements, as well as ensure the continuity of critical nodes.
The authors propose the use of intelligent agents to monitor the functional state of network elements
and detect anomalies. The first stage of monitoring involves detecting violations at the local level, while
subsequent stages clarify the degree and type of violation. This approach makes it possible to increase the
reliability of the ITCM functioning, reduce the response time to critical situations and reduce the volume
of data circulation in the network, which is an important factor for distributed networks with limited
resources.

Keywords: information network, monitoring subsystem, information transfer, heterogeneous
networks, intelligent agents

List of used literature:
1. A Software Deep Packet Inspection System for Network Traffic Analysis and Anomaly
Detection / W. Song et al. Sensors. 2020. Vol. 20, no. 6. P. 1637. URL:
https://doi.org/10.3390/s20061637.
2. IMPROVING THE QUALITY OF HETEROGENEOUS TELECOMMUNICATION
NETWORKS WITH THE HELP OF FORECAST-BASED RESOURCE ALLOCATION.
Telecommunication and Information Technologies. 2024. Vol. 82, no. 1. URL:
https://doi.org/10.31673/2412-4338.2024.018894.
3. Machine Learning in Network Anomaly Detection: A Survey / S. Wang et al. IEEE Access.
2021. Vol. 9. P. 152379–152396. URL: https://doi.org/10.1109/access.2021.3126834.
4. Trace-Ability and Security Detection of Container Image Based on Inheritance Graph, Y.
Zheng, W. Dong and J. Zhao et al. IEEE Access. 2021 IEEE 5th International Conference on
Cryptography, Security and Privacy (CSP), Zhuhai, China, 2021, pp. 186-192, URL:
https://doi.org/10.1109/CSP51677.2021.9357496.
5. Cучасні технології безпроводового доступу в телекомунікаційних системах / Стелюк,
Б.Б., Костенко В.В., Семененко О.А. Міжнародна наукова конференція «Інноваційні технології,
моделі управління кібербезпекою ITMK-2021» 14-16 квітня 2021. Збірник тез, Дніпро, 2021, с.
13
6. Підвищення ефективності гетерогенних телекомунікаційних мереж / Васильківський
М.В., Чанхао Ю. (2019) (Doctoral dissertation, ВНТУ) с. 74-79.
7. Report to the Nations on Occupational Fraud and Abuse. URL:
https://www.acfe.com/rttn/docs/ 2014-report-to-nations.pdf
8. Probabilistic Graphical Models: Principles and Techniques. The MIT Press, 2009. p. 1231. Nir
Friedman D. K.
9. Detection of abrupt changes: Theory and application. Englewood Cliffs, N.J : Prentice Hall,
1993. P. 528. Basseville M.
10. Paradigms for Mobile Agent-Based Active Monitoring of Network Systems
https://ajanta.cs.umn.edu/papers/monitoring-paradigms.pdf.
11. "Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence"/ Jacques
Ferber. Addison-Wesley, 1999