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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Basu, K., Saeed, S.M., Pilato, C., Ashraf, M., Nabeel, M.T., Chakrabarty, K., Karri, R.: Cad-base: An attack vector into the electronics supply chain. ACM Trans. Design Autom. Electron Syst. 24(4), 1–30 (2019)
Bickford, J., Van Bossuyt, D.L., Beery, P., Pollman, A.: Operationalizing digital twins through model-based systems engineering methods. Syst. Eng. 23(6), 724–750 (2020)
Brown, T.B., Mané, D., Roy, A., Abadi, M., Gilmer, J.: Adversarial patch (2018)
Buchanan, B.G.: A (very) brief history of artificial intelligence. Ai Mag. 26(4), 53–53 (2005)
Datta, A., Franklin, J., Garg, D., Jia, L., Kaynar, D.K.: On adversary models and compositional security. IEEE Secur. Privacy 9, 26–32 (2011)
Davies, R.: Industry 4.0: Digitalisation for productivity and growth. Briefing PE 568.337, European Parliamentary Research Service (2015)
Fingas, J.: Hackers conduct one of the largest supply chain cyberattacks to date: a breach at Kaseya has affected over 200 companies. Engadget (2021). https://www.engadget.com/kaseya-ransomware-cyberattack-155719139.html
Goebel, R., Chander, A., Holzinger, K., Lecue, F., Akata, Z., Stumpf, S., Kieseberg, P., Holzinger, A.: Explainable ai: the new 42? In: International Cross-Domain Conference for Machine Learning and Knowledge Extraction, pp. 295–303. Springer, Berlin (2018)
Goodfellow, I., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples (2015). CoRR abs/1412.6572
Gran, B.A., Fredriksen, R., Thunem, A.P.J.: An approach for model-based risk assessment. In: International Conference on Computer Safety, Reliability, and Security, pp. 311–324. Springer, Berlin (2004)
Hale, B., Van Bossuyt, D.L., Papakonstantinou, N., O’Halloran, B.: A zero-trust methodology for security of complex systems with machine learning components. In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers (2021)
Kavak, H., Padilla, J.J., Vernon-Bido, D., Diallo, S.Y., Gore, R., Shetty, S.: Simulation for cybersecurity: state of the art and future directions. J. Cybersecur. 7, tyab005 (2021)
Kovacs, E.: Tesla car hacked remotely from drone via zero-click exploit. Security Week (2020). https://www.securityweek.com/tesla-car-hacked-remotely-drone-zero-click-exploit
Liu, Y., Mondal, A., Chakraborty, A., Zuzak, M., Jacobsen, N., Xing, D., Srivastava, A.: A survey on neural trojans. In: 2020 21st International Symposium on Quality Electronic Design (ISQED), pp. 33–39 (2020)
MarketsandMarkets Research Staff: Artificial intelligence market by offering (hardware, software, services), technology (machine learning, natural language processing), deployment mode, organization size, business function (law, security), vertical, and region—global forecast to 2026. Tech. rep., MarketsandMarkets (2021). https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-market-74851580.html
Mili, S., Nguyen, N., Chelouah, R.: Model-driven architecture based security analysis. Systems Engineering (2021)
Myagmar, S., Lee, A.J., Yurcik, W.: Threat modeling as a basis for security requirements. In: Symposium on Requirements Engineering for Information Security (SREIS) (2005). http://d-scholarship.pitt.edu/16516/
Object Management Group Inc.: Welcome to UML Web Site! Accessed 12 Aug 2021. https://www.uml.org/
Papakonstantinou, N., Hale, B., Linnosmaa, J., Salonen, J., Van Bossuyt, D.L.: Model driven engineering for resilience of systems with black box and ai-based components. In: Annual Reliability and Maintainability Symposium (RAMS). IEEE, Piscataway (2022)
Papakonstantinou, N., Van Bossuyt, D.L., Linnosmaa, J., Hale, B., O’Halloran, B.: A zero trust hybrid security and safety risk analysis method. J. Comput. Inform. Sci. Eng. 21(5), 050907 (2021)
Pidd, M.: Systems Modelling: Theory and Practice. Wiley, New York (2004)
Ruegamer, A., Kowalewski, D., et al.: Jamming and spoofing of GNSS signals–an underestimated risk?! Proc. Wisdom Ages Challenges Modern World 3, 17–21 (2015)
Russell, S.J., Stuart, J.: Norvig. Artificial Intelligence: A Modern Approach, pp. 111–114 (2003)
Samuel, A.L.: Some studies in machine learning using the game of checkers. IBM J. Res. Dev. 3(3), 210–229 (1959)
Schaeffer, J.: Didn’t Samuel solve that game? In: One Jump Ahead, pp. 1–11. Springer, Berlin (2009)
Shane, S., Sanger, D.E.: Drone Crash in Iran Reveals Secret Us Surveillance Effort. The New York Times, vol. 7 (2011)
Stamatelatos, M., Dezfuli, H., Apostolakis, G., Everline, C., Guarro, S., Mathias, D., Mosleh, A., Paulos, T., Riha, D., Smith, C., et al.: Probabilistic risk assessment procedures guide for NASA managers and practitioners. Tech. rep., National Air and Space Administration (2011)
SysML.Org: SysML Open Source Project—What is SysML? Who created SysML? Accessed 12 Aug 2021. https://sysml.org/
Systems Engineering Research Consortium: Model based systems engineering (mbse) (2020). https://www.nasa.gov/consortium/ModelBasedSystems
Tao, F., Zhang, H., Liu, A., Nee, A.Y.C.: Digital twin in industry: state-of-the-art. IEEE Trans. Ind. Inform. 15(4), 2405–2415 (2019). https://doi.org/10.1109/TII.2018.2873186
US Nuclear Regulatory Commission: Probabilistic risk assessment (pra) (2020). https://www.nrc.gov/about-nrc/regulatory/risk-informed/pra.html
Xiang, Z., Miller, D.J., Kesidis, G.: A benchmark study of backdoor data poisoning defenses for deep neural network classifiers and A novel defense. In: 29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019, Pittsburgh, PA, USA, October 13–16, 2019, pp. 1–6. IEEE, Piscataway (2019)
Yamin, M.M., Ullah, M., Ullah, H., Katt, B.: Weaponized ai for cyber attacks. J. Inform. Secur. Appl. 57, 102722 (2021). https://doi.org/10.1016/j.jisa.2020.102722
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Van Bossuyt, D.L., Papakonstantinou, N., Hale, B., Salonen, J., O’Halloran, B. (2023). Model Based Resilience Engineering for Design and Assessment of Mission Critical Systems Containing Artificial Intelligence Components. In: Sipola, T., Kokkonen, T., Karjalainen, M. (eds) Artificial Intelligence and Cybersecurity. Springer, Cham. https://doi.org/10.1007/978-3-031-15030-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-15030-2_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15029-6
Online ISBN: 978-3-031-15030-2
eBook Packages: Computer ScienceComputer Science (R0)