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Detecting and Preventing Faked Mixed Reality
4th IEEE International Conference on Multimedia Information Processing and Retrieval, MIPR 2021 ; : 399-405, 2021.
Article in English | Scopus | ID: covidwho-1752420
ABSTRACT
Virtualized collaboration can significantly increase remote management of critical infrastructures. Crises such as the current COVID-19 pandemic push the technology they require remote management to keep our infrastructures running. Mixed Reality (MR) prototypes enable remote management in diverse fields such as medicine, industry 4.0, energy systems, education, or cyber awareness. However, the evolution of virtualized collaboration is still in the beginning. By design, MR is fake its reality is generated from models. This makes detecting attacks very difficult. Many MR-attacks result from well-known cybersecurity threats. This paper identifies classic attack surfaces, vectors, and concrete threats that are relevant for MR. It presents mitigation methods that can help to secure the underlying data exchanges. However, distributed systems are often heterogeneous and under different management authorities, making securing the entire virtualized remote management stack difficult. The paper therefore also introduces considerations towards an MR-client-based attack detection, i.e., MR-forensics, including relevant features and the use of machine learning. © 2021 IEEE.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 4th IEEE International Conference on Multimedia Information Processing and Retrieval, MIPR 2021 Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 4th IEEE International Conference on Multimedia Information Processing and Retrieval, MIPR 2021 Year: 2021 Document Type: Article