Leveraging Digital Twin Technologies for Public Space Protection and Vulnerability Assessment

TL;DR

The paper addresses the protection of soft-target public spaces against evolving, hybrid threats by introducing DTaaSS, a cloud-based Digital Twin framework that fuses Digital Twins with IoT, cloud computing, Big Data analytics, and AI. It delivers data collection, area monitoring, proactive threat detection, incident prediction, and data-driven vulnerability assessment for large-scale environments such as metro stations, leisure sites, and cathedral squares. The architecture emphasizes multi-source data fusion, AI-powered threat detection, and operational knowledge to bridge technology with field practices, demonstrated through three representative attack scenarios. It also outlines future directions including advanced simulation environments and privacy considerations to ensure practical and ethical deployment. Overall, the work advances the integration of digital twin technology into security infrastructures for robust, real-time protection of public spaces.

Abstract

Over the recent years, the protection of the so-called `soft-targets', i.e. locations easily accessible by the general public with relatively low, though, security measures, has emerged as a rather challenging and increasingly important issue. The complexity and seriousness of this security threat growths nowadays exponentially, due to the emergence of new advanced technologies (e.g. Artificial Intelligence (AI), Autonomous Vehicles (AVs), 3D printing, etc.); especially when it comes to large-scale, popular and diverse public spaces. In this paper, a novel Digital Twin-as-a-Security-Service (DTaaSS) architecture is introduced for holistically and significantly enhancing the protection of public spaces (e.g. metro stations, leisure sites, urban squares, etc.). The proposed framework combines a Digital Twin (DT) conceptualization with additional cutting-edge technologies, including Internet of Things (IoT), cloud computing, Big Data analytics and AI. In particular, DTaaSS comprises a holistic, real-time, large-scale, comprehensive and data-driven security solution for the efficient/robust protection of public spaces, supporting: a) data collection and analytics, b) area monitoring/control and proactive threat detection, c) incident/attack prediction, and d) quantitative and data-driven vulnerability assessment. Overall, the designed architecture exhibits increased potential in handling complex, hybrid and combined threats over large, critical and popular soft-targets. The applicability and robustness of DTaaSS is discussed in detail against representative and diverse real-world application scenarios, including complex attacks to: a) a metro station, b) a leisure site, and c) a cathedral square.

Figures (3)

• Figure 1: Categorization of DT applications in the security domain
• Figure 2: Proposed DTaaSS architecture for public space protection
• Figure 3: Primary groups of entities in the real world layer