Modeling of threats to critical infrastructure objects for the creation of defenses against swarm attacks by strike drones

Modern technologies used in hybrid warfare allow the enemy to perform large-scale attacks on critical infrastructure (e.g., transport, energy, industrial enterprises, etc.), which affects the country's economy. The use of unmanned aerial vehicles (UAVs) by the enemy in the form of swarms of strike drones has significantly increased the risk of damage to critical infrastructure objects (CIOs), which degrade over time and have dangerous vulnerabilities. Therefore, modeling defensive measures to protect CIOs from massive enemy air attacks is important. The subject of this study is the creation of mathematical and simulation models for planning preventive measures in CIO defense. This research aims to create a set of models that can be used to analyze and identify threatening vulnerabilities of critical infrastructure objects triggered by wave attacks of enemy strike drones and to plan appropriate preventive defensive actions under limited capabilities. The tasks to be solved are as follows: conduct a systematic analysis of the sequence of actions for planning CIO defense; identify the most threatening vulnerabilities of CIOs that could be attacked by the enemy; make a rational distribution of defense capabilities to protect CIO, taking into account the enemy’s capabilities to create strike capabilities using combat drones; and develop a multi-agent simulation model to analyze possible CIO damage from massive attacks by strike drones. Mathematical methods and models used: system analysis of preventive measures for CIO defense; qualitative assessment of CIO indicators to identify threatening vulnerabilities; lexicographic ordering of options to identify a set of relevant CIOs that need to be defended; integer (Boolean) optimization for the rational distribution of limited defense capabilities across locations of threatening CIO vulnerabilities; multi-agent simulation modeling of enemy wave drone attacks to analyze CIO damage. The following results were achieved: a logical sequence of preventive measures for CIO defense has been formed; a set of threatening vulnerabilities in CIO has been identified; the strike potential and capabilities of the enemy in terms of CIO vulnerability have been analyzed; the necessary defense potential for protecting CIO has been formed; various scenarios of strike drone attacks and possible CIO damage have been modeled; and a rational distribution of anti-drone means has been carried out to reduce the risks of CIO damage. Conclusions: the proposed set of models allows us to justify the logical sequence of preventive measures to defend CIOs against massive wave attacks by enemy strike drones. This will ensure the effective use of existing anti-drone defense measures against enemy attacks, even under conditions of limited capabilities. The scientific novelty of the proposed approach lies in the justification of protective measures for critical infrastructure against massive wave attacks by enemy strike drones based on a set of original and new mathematical and simulation models.