MODELING THE SURVIVABILITY AND RECOVERY OF INFORMATION AND COMMUNICATION NETWORKS IN THE FACE OF CYBER THREATS
DOI:
https://doi.org/10.32689/maup.it.2024.1.2Keywords:
information and communication systems, resilience, survivability, cognitive simulation model, recovery modeling, cyberattacks, infrastructure networksAbstract
The article examines the pressing issue of centralization and hierarchization in information and communication networks (ICNs), which has led to the development of methods for assessing and addressing weaknesses in the physical and functional infrastructure of ICNs. The advancement of modern ICNs and the services based on them has resulted in such complexity and centralization that global systemic failures and cascading scenarios of ICN unavailability in the future become inevitable. It has been established that existing methods for modeling ICN recovery can be divided into two categories: recovery factor analysis and analysis of the structural and/or functional model of the system. It is proven that ICNs have a distributed architecture, but there is a tendency towards centralization and hierarchization. Attention is paid to the specificity of ICNs as an infrastructure object. The paper proposes a cognitive-emulation model for ICN recovery (CSM ICN), which is based on the general CSM CTS model and is used for comprehensive forecasting and scenario modeling of possible failures and unavailability scenarios of ICNs. CSM ICN utilizes a directed graph (digraph) to model ICN components and their connections. The model can be “network-centric” (emphasizing connections) or hybrid (adding “virtual nodes” for cause-and-effect relationships). Each node models a part of the ICN, its element, or working component. Nodes have characteristics that include recovery time and nature. The model uses the Burnbaum criterion to assess the impact of one element’s failure on others. Node recovery is modeled as a variable number of discrete time steps. The model uses three types of recovery functions for different levels of system readiness and has 4 levels of evaluation. The model can assess cascading scenarios where the failure of one element leads to failures in other parts of the ICN. Conclusions. Thus, modeling recovery and restart scenarios of ICNs will help identify potential vulnerabilities, analyze cascading scenarios of ICN unavailability in case of emergencies and natural disasters. The application of CTS ICN will significantly increase the resilience and operational stability of ICNs in conditions of external attacks, personnel errors, and the impact of other emergencies and force majeure events.
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