Model Based Resilience Engineering for Design and Assessment of Mission Critical Systems Containing Artificial Intelligence Components

Douglas Van Bossuyt; Nikolaos Papakonstantinou; Britta Hale; Jarno Salonen; Bryan M. O'Halloran

doi:10.1007/978-3-031-15030-2_3

Model Based Resilience Engineering for Design and Assessment of Mission Critical Systems Containing Artificial Intelligence Components

Douglas Van Bossuyt^*, Nikolaos Papakonstantinou, Britta Hale, Jarno Salonen, Bryan M. O'Halloran

^*Corresponding author for this work

BA6403 Cybersecurity engineering and automation

Naval Postgraduate School

Research output: Chapter in Book/Report/Conference proceeding › Chapter or book article › Scientific › peer-review

3 Citations (Scopus)

Abstract

Critical system resilience is a focus point in risk management due to the severe consequences of system failure. As such critical systems become increasingly cyber-physical, cybersecurity vulnerabilities also play a more significant role in system analysis. Now, as the world turns to Artificial Intelligence (AI)-based solutions, the novel vulnerabilities in AI and other black box components add a complexity dimension to the assessment of cyber-physical critical system analysis. This chapter provides a closer look at a Model Based Systems Engineering (MBSE) approach to assess and design the resilience of complex systems throughout their life cycle. As a case study of a system of systems comprised of Unmanned Aerial Systems (UASs) and Unmanned Surface Vessels (USVs) for littoral zone patrol is presented with a focus on system resilience under a national security mission.

Original language	English
Title of host publication	Artificial Intelligence and Cybersecurity
Subtitle of host publication	Theory and Applications
Editors	Tuomo Sipola, Tero Kokkonen, Mika Karjalainen
Publisher	Springer
Pages	47-66
ISBN (Electronic)	978-3-031-15030-2
ISBN (Print)	978-3-031-15029-6, 978-3-031-15032-6
DOIs	https://doi.org/10.1007/978-3-031-15030-2_3
Publication status	Published - 1 Aug 2022
MoE publication type	A3 Part of a book or another research book

Keywords

Artificial Intelligence
Privacy
Cryptology
Mobile and Network Security

Access to Document

10.1007/978-3-031-15030-2_3

Cite this

Van Bossuyt, D., Papakonstantinou, N., Hale, B., Salonen, J., & O'Halloran, B. M. (2022). Model Based Resilience Engineering for Design and Assessment of Mission Critical Systems Containing Artificial Intelligence Components. In T. Sipola, T. Kokkonen, & M. Karjalainen (Eds.), Artificial Intelligence and Cybersecurity: Theory and Applications (pp. 47-66). Article 3 Springer. https://doi.org/10.1007/978-3-031-15030-2_3

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Van Bossuyt, D, Papakonstantinou, N, Hale, B, Salonen, J & O'Halloran, BM 2022, Model Based Resilience Engineering for Design and Assessment of Mission Critical Systems Containing Artificial Intelligence Components. in T Sipola, T Kokkonen & M Karjalainen (eds), Artificial Intelligence and Cybersecurity: Theory and Applications., 3, Springer, pp. 47-66. https://doi.org/10.1007/978-3-031-15030-2_3

Model Based Resilience Engineering for Design and Assessment of Mission Critical Systems Containing Artificial Intelligence Components. / Van Bossuyt, Douglas; Papakonstantinou, Nikolaos; Hale, Britta et al.
Artificial Intelligence and Cybersecurity: Theory and Applications. ed. / Tuomo Sipola; Tero Kokkonen; Mika Karjalainen. Springer, 2022. p. 47-66 3.

Research output: Chapter in Book/Report/Conference proceeding › Chapter or book article › Scientific › peer-review

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AU - Van Bossuyt, Douglas

AU - Papakonstantinou, Nikolaos

AU - Hale, Britta

AU - Salonen, Jarno

AU - O'Halloran, Bryan M.

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

PY - 2022/8/1

Y1 - 2022/8/1

N2 - Critical system resilience is a focus point in risk management due to the severe consequences of system failure. As such critical systems become increasingly cyber-physical, cybersecurity vulnerabilities also play a more significant role in system analysis. Now, as the world turns to Artificial Intelligence (AI)-based solutions, the novel vulnerabilities in AI and other black box components add a complexity dimension to the assessment of cyber-physical critical system analysis. This chapter provides a closer look at a Model Based Systems Engineering (MBSE) approach to assess and design the resilience of complex systems throughout their life cycle. As a case study of a system of systems comprised of Unmanned Aerial Systems (UASs) and Unmanned Surface Vessels (USVs) for littoral zone patrol is presented with a focus on system resilience under a national security mission.

AB - Critical system resilience is a focus point in risk management due to the severe consequences of system failure. As such critical systems become increasingly cyber-physical, cybersecurity vulnerabilities also play a more significant role in system analysis. Now, as the world turns to Artificial Intelligence (AI)-based solutions, the novel vulnerabilities in AI and other black box components add a complexity dimension to the assessment of cyber-physical critical system analysis. This chapter provides a closer look at a Model Based Systems Engineering (MBSE) approach to assess and design the resilience of complex systems throughout their life cycle. As a case study of a system of systems comprised of Unmanned Aerial Systems (UASs) and Unmanned Surface Vessels (USVs) for littoral zone patrol is presented with a focus on system resilience under a national security mission.

KW - Artificial Intelligence

KW - Privacy

KW - Cryptology

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Van Bossuyt D, Papakonstantinou N, Hale B, Salonen J, O'Halloran BM. Model Based Resilience Engineering for Design and Assessment of Mission Critical Systems Containing Artificial Intelligence Components. In Sipola T, Kokkonen T, Karjalainen M, editors, Artificial Intelligence and Cybersecurity: Theory and Applications. Springer. 2022. p. 47-66. 3 doi: 10.1007/978-3-031-15030-2_3

Model Based Resilience Engineering for Design and Assessment of Mission Critical Systems Containing Artificial Intelligence Components

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