Abstract
Critical infrastructure is high-level priority area in the conditions of a hybrid large-scale war in Ukraine. Real-time threats monitoring should be provided in both cyber and physical space. This study analyzes existing machine learning (ML) algorithms in real time, compared their advantages and disadvantages, determined the main criteria and chose the optimal algorithm for protecting people and critical infrastructure in the conditions of a hybrid large- scale war. The optimal algorithm was defined by criteria; it can be quickly learned detect the desired objects in a limited time, has good object finding accuracy, and does not require large server capacities. The novelty of the work lies in multicriteria analysis of real-time object detection algorithms by proposed criteria as well as defining optimal algorithm that can be used to detect dangerous objects in critical infrastructures. Given results will be useful for developing ML-based real-time monitoring system for critical infrastructure.