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Development of an Information Security System Based on Modeling Distributed Computer Network Vulnerability Indicators of an Informatization Object

Lakhno Valerii, Alimseitova Zhuldyz, Kalaman Yerbolat, Kryvoruchko Olena, Desiatko Alona, Kaminskyi Serhii

International Journal of Electronics and Telecommunications

2023

Vol. 69, No. 3

475--483

A methodology for development for distributed computer network (DCN) information security system (IS) for an informatization object (OBI) was proposed. It was proposed to use mathematical modeling at the first stage of the methodology. In particular, a mathematical model was presented based on the use of the apparatus of probability theory to calculate the vulnerability coefficient. This coefficient allows one to assess the level of information security of the OBI network. Criteria for assessing the acceptable and critical level of risks for information security were proposed as well. At the second stage of the methodology development of the IS DCN system, methods of simulation and virtualization of the components of the IS DCN were used. In the course of experimental studies, a model of a protected DCN has been built. In the experimental model, network devices and DCN IS components were emulated on virtual machines (VMs). The DCN resources were reproduced using the Proxmox VE virtualization system. IPS Suricata was deployed on RCS hosts running PVE. Splunk was used as SIEM. It has been shown that the proposed methodology for the formation of the IS system for DCN and the model of the vulnerability coefficient makes it possible to obtain a quantitative assessment of the levels of vulnerability of DCN OBI.

Automation of Information Security Risk Assessment

Akhmetov Berik, Lakhno Valerii, Chubaievskyi Vitalyi, Kaminskyi Serhii, Adilzhanova Saltanat, Ydyryshbayeva Moldir

International Journal of Electronics and Telecommunications

2022

Vol. 68. No. 3

549--555

An information security audit method (ISA) for a distributed computer network (DCN) of an informatization object (OBI) has been developed. Proposed method is based on the ISA procedures automation by using Bayesian networks (BN) and artificial neural networks (ANN) to assess the risks. It was shown that such a combination of BN and ANN makes it possible to quickly determine the actual risks for OBI information security (IS). At the same time, data from sensors of various hardware and software information security means (ISM) in the OBI DCS segments are used as the initial information. It was shown that the automation of ISA procedures based on the use of BN and ANN allows the DCN IS administrator to respond dynamically to threats in a real time manner, to promptly select effective countermeasures to protect the DCS.