Multimodal Platform for Early Detection and Response to Unmanned Threats as an Element of Security Architecture

The full-scale war in Ukraine has become one of the first global examples of the systematic use of unmanned technologies in an interstate conflict. In this regard, the development of new conceptual models of early detection systems for drone threats, capable of combining different types of sensors, data analysis algorithms, and decision support systems, is of particular importance. The purpose of this article is to analyze the potential of a multimodal early detection platform for drone threats for national and global security. The proposed model is a modular, multi-level architecture for detecting spatial anomalies and supporting decision-making, based on the principle of integrating various sensor technologies into a single situational awareness architecture. The main innovation of the system is the inverse detection logic, which is combined with parallel multi-sensor verification and involves the simultaneous operation of several confirmation technologies, as well as bringing the system into a state of readiness in advance of decision-making. The key features of this model are the expansion of detection capabilities to identify swarms of drones, limiting the role of artificial intelligence exclusively to recognizing types of drone threats, predicting their behavior, and developing response scenarios, and the exclusive function of humans in making the final decision on how to respond. This approach allows:• using GSM and GNSS network data;• reducing threat confirmation time;• improving system response readiness;• refining the parameters and characteristics of drone threats;• protecting against AI misjudgments and loss of human control. Conceptual Origin:The analytical frameworks exploring sensing ecosystems, swarm governance and dual-use security architectures described in this document, including the concept of the DRONEDOME Platform within DRONEDOME Center prior to any collaboration or institutional engagement described herein. Their inclusion in academic, research, or collaborative contexts does not imply transfer of intellectual property or institutional ownership. Attribution of individual scholarly contribution may be made in academic settings, while institutional intellectual property remains with institutional intellectual property held by DRONEDOME as the originating research entity and individual scholarly contributions attributed to Oleh Deineka, Volodymyr Khomenko, and Sergiy Skidanov.