TY - GEN
T1 - Detecting and Preventing Faked Mixed Reality
AU - Kilger, Fabian
AU - Kabil, Alexandre
AU - Tippmann, Volker
AU - Klinker, Gudrun
AU - Pahl, Marc Oliver
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Cybersecurity
KW - Deepfake
KW - MR forensics
KW - Mixed Reality
KW - Remote Management
UR - https://www.scopus.com/pages/publications/85126226055
U2 - 10.1109/MIPR51284.2021.00074
DO - 10.1109/MIPR51284.2021.00074
M3 - Conference contribution
AN - SCOPUS:85126226055
T3 - Proceedings - 4th International Conference on Multimedia Information Processing and Retrieval, MIPR 2021
SP - 399
EP - 405
BT - Proceedings - 4th International Conference on Multimedia Information Processing and Retrieval, MIPR 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Multimedia Information Processing and Retrieval, MIPR 2021
Y2 - 8 September 2021 through 10 September 2021
ER -