Abstract
Cyber Physical Systems (CPS) are attracting intense research interest due to the explosive availability of data and connectivity. The Dynamic Data Driven Applications Systems (DDDAS) paradigm provides a suitable framework for solutions to the risks of connectivity through big data and machine learning. Computational and measurement data come together to produce integrative yet discriminating features and patterns amenable to machine learning and Artificial Intelligence (AI) decision approaches and thus DDDAS and CPSs bridge the physical with the cyber world in numerous applications principally anchored in unique physics.
DDDAS is of great value in prioritizing and categorizing data in accordance with system dynamics. The physical aspect of CPSs is considered as an advantage, as the inherited inertia of systems like these affords additional time for processing and protective activities. This characteristic proves helpful towards intrusion detection and projection of failures as well as buttressing the system with defense-in-depth capabilities demonstrated through effectively achieving Byzantine Fault Tolerance.
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Acknowledgement
This work is supported in part by ONR under Grant No N00014-18-1-2278, the US Department of Energy under Grant No. 2014-0501-03 and a GS-Gives grant to AI Systems Lab (AISL).
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Pantopoulou, S., Lagari, P.L., Townsend, C.H., Tsoukalas, L.H. (2020). Data-Based Defense-in-Depth of Critical Systems. In: Darema, F., Blasch, E., Ravela, S., Aved, A. (eds) Dynamic Data Driven Applications Systems. DDDAS 2020. Lecture Notes in Computer Science(), vol 12312. Springer, Cham. https://doi.org/10.1007/978-3-030-61725-7_33
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DOI: https://doi.org/10.1007/978-3-030-61725-7_33
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