Improving the technology for processing the aggregated data flow of a secure corporate multiservice communication network

Authors

DOI:

https://doi.org/10.15587/1729-4061.2023.285414

Keywords:

secure corporate multiservice communication network, aggregate flow, channel resource, VPN gateway

Abstract

This paper considers the process of dynamic reservation of the channel resource of a secure corporate multi-service communication network.

It has been established that the processes of building and functioning of the schemes of a secure corporate multi-service communication network and improving the quality of the implementation of its main work processes involve the evaluation and dynamic reservation of channel resources for incoming aggregated data flows of the network.

The model of dynamic reservation of the channel resource of the aggregated data stream of the secure corporate multi-service communication network was built and proposed. The proposed model makes it possible to set the quantitative values of the reserved channel resource for different service methods depending on the number of component flows in the total aggregated data flow of the VPN tunnel.

It was established that an increase in the density of the aggregated data stream requires an increase in the reserved channel resource. At the same time, its value is influenced by the way of servicing the aggregated data flow in the VPN tunnel of the secure corporate multi-service communication network. Application of the isolated service method gives a gain in the allocated resource for the channel reserve from 10 to 20 percent compared to the group service method for IR and video telephony. This is due to the more flexible management process of the border router's incoming data storage buffer under the isolated service mode.

The model of dynamic reservation of the channel resource of the secure corporate multi-service communication network reported in this paper could be used in the improvement of existing and development of new structures of the secure corporate multi-service communication network. The consequence of such an improvement is a reduction in the delay time for the processing of incoming data packets in the specified network

Author Biographies

Liubov Berkman, State University of Telecommunications

Doctor of Technical Sciences, Professor, Vice-Rector for Educational and Scientific Work 

Andrii Zakharzhevskyi, The National Defence University of Ukraine named after Ivan Cherniakhovskyi

PhD

Department of National Security And Defence Strategy

Kostiantyn Lavrinets, State University of Telecommunications

PhD, Associate Professor

Department of Telecommunication Systems and Networks

References

  1. Popivskyi, V. V., Lemeshko, O. V., Kovalchuk, V. K. Plotnikov, M. D., Kartushyn, Yu. P. et al. (2012). Telekomunikatsiini systemy ta merezhi. Struktura y osnovni funktsiyi. Vol. 1.
  2. Zakhyst informatsiyi na obiektakh informatsiynoi diyalnosti. Stvorennia kompleksu tekhnichnoho zakhystu informatsiyi. Osnovni polozhennia. ND TZI 1.1-005-07. Available at: https://tzi.com.ua/nd-tz-1.1-005-07.html
  3. Halkin, V. V., Parkhomenko, I. I. (2016). Vykorystannia VPN-tekhnolohiy dlia zakhystu informatsiyi v kanalakh korporatyvnykh merezh. Problema kiberbezpeky informatsiyno-telekomunikatsiynykh system: materialy nauk.- tekhn. konf. Kyiv: KNU, 66–76.
  4. Buriachok, V. L., Anosov, A. O., Semko, V. V., Sokolov, V. Yu., Skladannyi, P. M. (2019). Tekhnolohiyi zabezpechennia bezpeky merezhevoi infrastruktury. Kyiv: «KUBH», 218. Available at: https://elibrary.kubg.edu.ua/id/eprint/27191/1/VL_Buriachok_TZBMI.pdf
  5. Popovskyi, V. V., Oliinyk, V. F. (2011). Matematychni osnovy upravlinnia i adaptatsiyi v telekomunikatsiynykh systemakh. Kharkiv: TOV “Kompaniya SMIT”, 362.
  6. IPSec – protokol zakhystu merezhevoho trafiku na IP-rivni.
  7. Talib, H. A., Alothman, R. B., Mohammed, M. S. (2023). Malicious attacks modelling: a prevention approach for ad hoc network security. Indonesian Journal of Electrical Engineering and Computer Science, 30 (3), 1856. doi: https://doi.org/10.11591/ijeecs.v30.i3.pp1856-1865
  8. Almomani, A. (2022). Classification of Virtual Private networks encrypted traffic using ensemble learning algorithms. Egyptian Informatics Journal, 23 (4), 57–68. doi: https://doi.org/10.1016/j.eij.2022.06.006
  9. Balachandran, A., Amritha, P. P. (2022). VPN Network Traffic Classification Using Entropy Estimation and Time-Related Features. Smart Innovation, Systems and Technologies, 509–520. doi: https://doi.org/10.1007/978-981-16-3945-6_50
  10. Ma, X., Zhu, W., Wei, J., Jin, Y., Gu, D., Wang, R. (2023). EETC: An extended encrypted traffic classification algorithm based on variant resnet network. Computers & Security, 128, 103175. doi: https://doi.org/10.1016/j.cose.2023.103175
  11. Naas, M., Fesl, J. (2023). A novel dataset for encrypted virtual private network traffic analysis. Data in Brief, 47, 108945. doi: https://doi.org/10.1016/j.dib.2023.108945
  12. Lemeshko, O., Lebedenko, T., Nevzorova, O., Snihurov, A., Mersni, A., Al-Dulaimi, A. (2019). Development of the Balanced Queue Management Scheme with Optimal Aggregation of Flows and Bandwidth Allocation. 2019 IEEE 15th International Conference on the Experience of Designing and Application of CAD Systems (CADSM). doi: https://doi.org/10.1109/cadsm.2019.8779246
  13. Patil, H. K., Chen, T. M. (2017). Wireless Sensor Network Security. Computer and Information Security Handbook, 317–337. doi: https://doi.org/10.1016/b978-0-12-803843-7.00018-1
  14. Afuwape, A. A., Xu, Y., Anajemba, J. H., Srivastava, G. (2021). Performance evaluation of secured network traffic classification using a machine learning approach. Computer Standards & Interfaces, 78, 103545. doi: https://doi.org/10.1016/j.csi.2021.103545
  15. Geyer, F., Scheffler, A., Bondorf, S. (2023). Network Calculus With Flow Prolongation – A Feedforward FIFO Analysis Enabled by ML. IEEE Transactions on Computers, 72 (1), 97–110. doi: https://doi.org/10.1109/tc.2022.3204225
  16. Kovalenko, A., Kuchuk, H., Tkachov, V. (2021). Method of ensuring the survivability of the computer network based on vpn-tunneling. Control, Navigation and Communication Systems. Academic Journal, 1 (63), 90–95. doi: https://doi.org/10.26906/sunz.2021.1.090
  17. Kuchuk, N., Gavrylenko, S., Sobchuk, V., Lukova-Chuiko, N. (2019). Redistribution of information flows in a hyperconvergent system. Advanced Information Systems, 3 (2), 116–121. doi: https://doi.org/10.20998/2522-9052.2019.2.20
  18. Svyrydov, A., Kovalenko, A., Kuchuk, H. (2018). The pass-through capacity redevelopment method of net critical section based on improvement ON/OFF models of traffic. Advanced Information Systems, 2 (2), 139–144. doi: https://doi.org/10.20998/2522-9052.2018.2.24
  19. ITU-T Technical Report. XSTR-SEC-MANUAL Security in telecommunications and information technology (7th edition) (2022). International Telecommunication Union. Available at: https://www.itu.int/dms_pub/itu-t/opb/tut/T-TUT-ICTSS-2020-4-PDF-E.pdf
  20. Y.1541: Network performance objectives for IP-based services (2011). Available at: https://www.itu.int/rec/T-REC-Y.1541-201112-I/en
  21. Hnatushenko, V. V. (2014) Modeliuvannia ahrehovanoho trafiku peredachi danykh na osnovi modeli ON/OFF. Systemni tekhnolohiyi, 5, 65–72. Available at: http://nbuv.gov.ua/UJRN/st_2014_5_10
  22. Lebedenko, T., Goloveshko, M., Holodkova, A. (2019). Investigation of the method of active queue management on the interfaces of telecommunication networks routers. Control, Navigation and Communication Systems. Academic Journal, 4 (56), 57–62. doi: https://doi.org/10.26906/sunz.2019.4.057
  23. Lebedenko, T., Goloveshko, M., Severilov, A. (2019). The results of the experimental study of the Active Queue Management method at the interfaces of telecommunication networks. Problems of Telecommunications, 2 (25), 37–55. doi: https://doi.org/10.30837/pt.2019.2.03
  24. Gnatyuk, S., Kinzeryavyy, V., Kyrychenko, K., Yubuzova, K., Aleksander, M., Odarchenko, R. (2019). Secure Hash Function Constructing for Future Communication Systems and Networks. Advances in Intelligent Systems and Computing, 561–569. doi: https://doi.org/10.1007/978-3-030-12082-5_51
  25. Brumnik, R., Kovtun, V., Okhrimenko, A., Kavun, S. (2014). Techniques for Performance Improvement of Integer Multiplication in Cryptographic Applications. Mathematical Problems in Engineering, 2014, 1–7. doi: https://doi.org/10.1155/2014/863617
  26. Odarchenko, R., Gnatyuk, V., Gnatyuk, S., Abakumova, A. (2018). Security Key Indicators Assessment for Modern Cellular Networks. 2018 IEEE First International Conference on System Analysis & Intelligent Computing (SAIC). doi: https://doi.org/10.1109/saic.2018.8516889
  27. Berkman, L., Turovsky, O., Kyrpach, L., Varfolomeeva, O., Dmytrenko, V., Pokotylo, O. (2021). Analyzing the code structures of multidimensional signals for a continuous information transmission channel. Eastern-European Journal of Enterprise Technologies, 5 (9 (113)), 70–81. doi: https://doi.org/10.15587/1729-4061.2021.242357
Improving the technology for processing the aggregated data flow of a secure corporate multiservice communication network

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Published

2023-08-31

How to Cite

Berkman, L., Zakharzhevskyi, A., & Lavrinets, K. (2023). Improving the technology for processing the aggregated data flow of a secure corporate multiservice communication network. Eastern-European Journal of Enterprise Technologies, 4(9 (124), 14–23. https://doi.org/10.15587/1729-4061.2023.285414

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Section

Information and controlling system