    @article {Alcaraz2022b,
	title = {Digital Twin: A Comprehensive Survey of Security Threats},
	journal = {IEEE Communications Surveys \& Tutorials},
	volume = {24},
	number = {thirdquarter 2022},
	year = {2022},
	month = {04/2022},
	pages = {1475 - 1503},
	publisher = {IEEE},
	type = {Survey},
	address = {IEEE },
	abstract = {<p class="rtejustify">Industry 4.0 is having an increasingly positive impact on the value chain by modernizing and optimizing the production and distribution processes. In this streamline, the digital twin (DT) is one of the most cutting-edge technologies of Industry 4.0, providing simulation capabilities to forecast, optimize and estimate states and configurations. In turn, these technological capabilities are encouraging industrial stakeholders to invest in the new paradigm, though an increased focus on the risks involved is really needed. More precisely, the deployment of a DT is based on the composition of technologies such as cyber-physical systems, the Industrial Internet of Things, edge computing, virtualization infrastructures, artificial intelligence and big data. However, the confluence of all these technologies and the implicit interaction with the physical counterpart of the DT in the real world generate multiple security threats that have not yet been sufficiently studied. In that context, this paper analyzes the current state of the DT paradigm and classifies the potential threats associated with it, taking into consideration its functionality layers and the operational requirements in order to achieve a more complete and useful classification. We also provide a preliminary set of security recommendations and approaches that can help to ensure the appropriate and trustworthy use of a DT.</p>
},
	keywords = {Cybersecurity, Digital Twin, Industry 4.0},
	issn = {1553-877X},
	doi = {https://doi.org/10.1109/COMST.2022.3171465},
	url = {https://ieeexplore.ieee.org/document/9765576},
	author = {Cristina Alcaraz and Javier Lopez}
}
@article {Flamini2022,
	title = {Towards Trustworthy Autonomous Systems: Taxonomies and Future Perspectives},
	journal = {IEEE Transactions on Emerging Topics in Computing},
	year = {2022},
	publisher = {IEEE},
	abstract = {<p class="rtejustify">The class of Trustworthy Autonomous Systems (TAS) includes cyber-physical systems leveraging on self-x technologies that make them capable to learn, adapt to changes, and reason under uncertainties in possibly critical applications and evolving environments. In the last decade, there has been a growing interest in enabling artificial intelligence technologies, such as advanced machine learning, new threats, such as adversarial attacks, and certification challenges, due to the lack of sufficient explainability. However, in order to be trustworthy, those systems also need to be dependable, secure, and resilient according to well-established taxonomies, methodologies, and tools. Therefore, several aspects need to be addressed for TAS, ranging from proper taxonomic classification to the identification of research opportunities and challenges. Given such a context, in this paper address relevant taxonomies and research perspectives in the field of TAS. We start from basic definitions and move towards future perspectives, regulations, and emerging technologies supporting development and operation of TAS.</p>
},
	keywords = {Arti cial Intelligence, Cyber- Resilience, Cybersecurity, Dependability, Intelligent Systems, Trustworthy Autonomous Systems},
	issn = {2168-6750},
	doi = {https://doi.org/10.1109/TETC.2022.3227113},
	url = {https://ieeexplore.ieee.org/abstract/document/9979717/authors$\#$authors},
	author = {Francesco Flammini and Cristina Alcaraz and Emanuele Bellini and Stefano Marrone and Javier Lopez and Andrea Bondavalli}
}
@article {Alcaraz2021a,
	title = {Stakeholder Perspectives and Requirements on Cybersecurity in Europe},
	journal = {Journal of Information Security and Applications},
	volume = {61},
	number = {102916},
	year = {2021},
	month = {09/2021},
	publisher = {Elsevier},
	keywords = {Cybersecurity, Requirements, Roadmap, Stakeholder engagement. Research \& innovation},
	issn = {2214-2126},
	doi = {https://doi.org/10.1016/j.jisa.2021.102916},
	url = {https://www.sciencedirect.com/science/article/pii/S2214212621001381},
	author = {Simone Fischer-H{\"u}bner and Cristina Alcaraz and Afonso Ferreira and Carmen Fernandez-Gago and Javier Lopez and Evangelos Markatos and Lejla Islami and Mahdi Akil}
}
