A New Concept of Digital Twin Supporting Optimization and Resilience of Factories of the Future

In the context of Industry 4.0, a growing use is being made of simulation-based decision-support tools commonly named Digital Twins. Digital Twins are replicas of the physical manufacturing assets, providing means for the monitoring and control of individual assets. Although extensive research on Digital Twins and their applications has been carried out, the majority of existing approaches are asset specific. Little consideration is made of human factors and interdependencies between different production assets are commonly ignored. In this paper, we address those limitations and propose innovations for cognitive modeling and co-simulation which may unleash novel uses of Digital Twins in Factories of the Future. We introduce a holistic Digital Twin approach, in which the factory is not represented by a set of separated Digital Twins but by a comprehensive modeling and simulation capacity embracing the full manufacturing process including external network dependencies. Furthermore, we introduce novel approaches for integrating models of human behavior and capacities for security testing with Digital Twins and show how the holistic Digital Twin can enable new services for the optimization and resilience of Factories of the Future. To illustrate this approach, we introduce a specific use-case implemented in field of Aerospace System Manufacturing.The present work was developed under the EUREKA–ITEA3 Project CyberFactory#1 (ITEA-17032), co-funded by Project CyberFactory#1PT (ANI|P2020 40124), from FEDER Funds through NORTE2020 program and from National Funds through FCT under the project UID/EEA/00760/2019 and by the Federal Ministry of Education and Research (BMBF, Germany, funding No. 01IS18061C).info:eu-repo/semantics/publishedVersio

Challenges and efforts in managing AI trustworthiness risks: a state of knowledge

This paper addresses the critical gaps in existing AI risk management frameworks, emphasizing the neglect of human factors and the absence of metrics for socially related or human threats. Drawing from insights provided by NIST AI RFM and ENISA, the research underscores the need for understanding the limitations of human-AI interaction and the development of ethical and social measurements. The paper explores various dimensions of trustworthiness, covering legislation, AI cyber threat intelligence, and characteristics of AI adversaries. It delves into technical threats and vulnerabilities, including data access, poisoning, and backdoors, highlighting the importance of collaboration between cybersecurity engineers, AI experts, and social-psychology-behavior-ethics professionals. Furthermore, the socio-psychological threats associated with AI integration into society are examined, addressing issues such as bias, misinformation, and privacy erosion. The manuscript proposes a comprehensive approach to AI trustworthiness, combining technical and social mitigation measures, standards, and ongoing research initiatives. Additionally, it introduces innovative defense strategies, such as cyber-social exercises, digital clones, and conversational agents, to enhance understanding of adversary profiles and fortify AI security. The paper concludes with a call for interdisciplinary collaboration, awareness campaigns, and continuous research efforts to create a robust and resilient AI ecosystem aligned with ethical standards and societal expectations

Simulation réaliste d'utilisateurs pour les systèmes d'information en Cyber Range

International audienceGenerating user activity is a key capability for both evaluating security monitoring tools as well as improving the credibility of attacker analysis platforms (e.g., honeynets). In this paper, to generate this activity, we instrument each machine by means of an external agent. This agent combines both deterministic and deep learning based methods to adapt to different environment (e.g., multiple OS, software versions, etc.), while maintaining high performances. We also propose conditional text generation models to facilitate the creation of conversations and documents to accelerate the definition of coherent, system-wide, life scenarios.La génération d'activité utilisateur est un élément-clé autant pour la qualification des produits de supervision de sécurité que pour la crédibilité des environnements d'analyse de l'attaquant. Ce travail aborde la génération automatique d'une telle activité en instrumentant chaque poste utilisateur à l'aide d'un agent externe; lequel combine des méthodes déterministes et d'apprentissage profond, qui le rendent adaptable à différents environnements, sans pour autant dégrader ses performances. La préparation de scénarios de vie cohérents à l'échelle du SI est assistée par des modèles de génération de conversations et de documents crédible

Simulation réaliste d'utilisateurs pour les systèmes d'information en Cyber Range

International audienceGenerating user activity is a key capability for both evaluating security monitoring tools as well as improving the credibility of attacker analysis platforms (e.g., honeynets). In this paper, to generate this activity, we instrument each machine by means of an external agent. This agent combines both deterministic and deep learning based methods to adapt to different environment (e.g., multiple OS, software versions, etc.), while maintaining high performances. We also propose conditional text generation models to facilitate the creation of conversations and documents to accelerate the definition of coherent, system-wide, life scenarios.La génération d'activité utilisateur est un élément-clé autant pour la qualification des produits de supervision de sécurité que pour la crédibilité des environnements d'analyse de l'attaquant. Ce travail aborde la génération automatique d'une telle activité en instrumentant chaque poste utilisateur à l'aide d'un agent externe; lequel combine des méthodes déterministes et d'apprentissage profond, qui le rendent adaptable à différents environnements, sans pour autant dégrader ses performances. La préparation de scénarios de vie cohérents à l'échelle du SI est assistée par des modèles de génération de conversations et de documents crédible

Digital Twins for Enhanced Resilience: Aerospace Manufacturing Scenario

International audienceEuropean manufacturing industry faces a growing cyber-threat landscape which increasingly involves sophisticated nation-state- sponsored actors. Moreover, while the ongoing evolution towards more connected production environments generates significant benefits in productivity, it also creates more complex risk scenarios. In this work we show how Digital Twins enhance the resilience and improve the overall security posture of manufacturing industry, based on a highly critical scenario unfolding in the aerospace sector

Actes de la conférence CAID 2021 (Conference on Artificial Intelligence for Defense)

6th European Cyber Week (6ème semaine européenne de la cybersécurité et de la cyberdéfense

Actes de la conférence CAID 2021 (Conference on Artificial Intelligence for Defense)

6th European Cyber Week (6ème semaine européenne de la cybersécurité et de la cyberdéfense

Actes de la conférence CAID 2021 (Conference on Artificial Intelligence for Defense)

6th European Cyber Week (6ème semaine européenne de la cybersécurité et de la cyberdéfense)International audienc

Actes de la conférence CAID 2021 (Conference on Artificial Intelligence for Defense)

6th European Cyber Week (6ème semaine européenne de la cybersécurité et de la cyberdéfense)International audienc

Actes de la conférence CAID 2021 (Conference on Artificial Intelligence for Defense)

6th European Cyber Week (6ème semaine européenne de la cybersécurité et de la cyberdéfense)International audienc