Leveraging Digital Twin Technologies for Public Space Protection and Vulnerability Assessment

• URL: http://arxiv.org/abs/2408.17136v1
• Date: Fri, 30 Aug 2024 09:27:12 GMT
• Title: Leveraging Digital Twin Technologies for Public Space Protection and Vulnerability Assessment
• Authors: Artemis Stefanidou, Jorgen Cani, Thomas Papadopoulos, Panagiotis Radoglou-Grammatikis, Panagiotis Sarigiannidis, Iraklis Varlamis, Georgios Th. Papadopoulos,
• Abstract summary: A Digital Twin-as-a-Security-Service (DTS) architecture is introduced for holistically enhancing the protection of public spaces. 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. Overall, the architecture exhibits increased potential in handling complex, hybrid and combined threats over large, critical and popular soft-targets.
• Score: 1.6556140382577318
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Enhancing Enterprise Security with Zero Trust Architecture [0.0]
  Zero Trust Architecture (ZTA) represents a transformative approach to modern cybersecurity. ZTA shifts the security paradigm by assuming that no user, device, or system can be trusted by default. This paper explores the key components of ZTA, such as identity and access management (IAM), micro-segmentation, continuous monitoring, and behavioral analytics.
  arXiv  Detail & Related papers  (2024-10-23T21:53:16Z)
• From 5G to 6G: A Survey on Security, Privacy, and Standardization Pathways [21.263571241047178]
  The vision for 6G aims to enhance network capabilities with faster data rates, near-zero latency, and higher capacity. This advancement seeks to enable immersive mixed-reality experiences, holographic communications, and smart city infrastructures. The expansion of 6G raises critical security and privacy concerns, such as unauthorized access and data breaches.
  arXiv  Detail & Related papers  (2024-10-04T03:03:44Z)
• Security in IS and social engineering -- an overview and state of the art [0.6345523830122166]
  The digitization of all processes and the opening to IoT devices has fostered the emergence of a new formof crime, i.e. cybercrime. The maliciousness of such attacks lies in the fact that they turn users into facilitators of cyber-attacks, to the point of being perceived as the weak link'' of cybersecurity. Knowing how to anticipate, identifying weak signals and outliers, detect early and react quickly to computer crime are therefore priority issues requiring a prevention and cooperation approach.
  arXiv  Detail & Related papers  (2024-06-17T13:25:27Z)
• Cyberattack Data Analysis in IoT Environments using Big Data [0.0]
  Our research addresses the growing connectivity and security challenges, including interoperability and standardized protocols. Our in-depth analysis of security vulnerabilities identified intricate patterns and threats, such as attack behavior, network traffic anomalies, TCP flag usage, and targeted attacks.
  arXiv  Detail & Related papers  (2024-06-14T02:12:43Z)
• Generative AI for Secure and Privacy-Preserving Mobile Crowdsensing [74.58071278710896]
  generative AI has attracted much attention from both academic and industrial fields. Secure and privacy-preserving mobile crowdsensing (SPPMCS) has been widely applied in data collection/ acquirement.
  arXiv  Detail & Related papers  (2024-05-17T04:00:58Z)
• Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
  Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks. We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
  arXiv  Detail & Related papers  (2024-02-21T06:22:41Z)
• The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective [64.36680481458868]
  Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge. This paper provides a survey of security and privacy in MEC from the perspective of Artificial Intelligence (AI) We focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI.
  arXiv  Detail & Related papers  (2024-01-03T07:47:22Z)
• Multi-Tier Computing-Enabled Digital Twin in 6G Networks [50.236861239246835]
  In Industry 4.0, industries such as manufacturing, automotive, and healthcare are rapidly adopting DT-based development. The main challenges to date have been the high demands on communication and computing resources, as well as privacy and security concerns. To achieve low latency and high security services in the emerging DT, multi-tier computing has been proposed by combining edge/fog computing and cloud computing.
  arXiv  Detail & Related papers  (2023-12-28T13:02:53Z)
• Scaling #DNN-Verification Tools with Efficient Bound Propagation and Parallel Computing [57.49021927832259]
  Deep Neural Networks (DNNs) are powerful tools that have shown extraordinary results in many scenarios. However, their intricate designs and lack of transparency raise safety concerns when applied in real-world applications. Formal Verification (FV) of DNNs has emerged as a valuable solution to provide provable guarantees on the safety aspect.
  arXiv  Detail & Related papers  (2023-12-10T13:51:25Z)
• Counter-terrorism in cyber-physical spaces: Best practices and technologies from the state of the art [3.072386223958412]
  The demand for protection and security of physical spaces and urban areas increased with the escalation of terroristic attacks in recent years. We envision with the proposed cyber-physical systems and spaces, a city that would indeed become a smarter urbanistic object, proactively providing alerts and being protective against any threat.
  arXiv  Detail & Related papers  (2023-11-28T18:06:30Z)
• Measurement-driven Security Analysis of Imperceptible Impersonation Attacks [54.727945432381716]
  We study the exploitability of Deep Neural Network-based Face Recognition systems. We show that factors such as skin color, gender, and age, impact the ability to carry out an attack on a specific target victim. We also study the feasibility of constructing universal attacks that are robust to different poses or views of the attacker's face.
  arXiv  Detail & Related papers  (2020-08-26T19:27:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.