Abstract
Modern cyber-physical systems require effective intrusion detection systems to ensure adequate critical infrastructure protection. Developing an intrusion detection capability requires an understanding of the behavior of a cyber-physical system and causality of its components. Such an understanding enables the characterization of normal behavior and the identification and reporting of anomalous behavior.
This chapter explores a relatively new time series analysis technique, empirical dynamic modeling, that can contribute to system understanding. Specifically, it examines if the technique can adequately describe causality in cyber-physical systems and provides insights into it serving as a foundation for intrusion detection.
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Association for Computing Machinery, ACM Transactions on Cyber-Physical Systems, New York (tcps.acm.org/about.cfm), 2020.
G. Boeing, Visual analysis of nonlinear dynamical systems: Chaos, fractals, self-similarity and the limits of prediction, Systems, vol. 4(4), article no. 37, 2016.
C. Chang, M. Ushio and C. Hsieh, Empirical dynamic modeling for beginners, Ecological Research, vol. 32(6), pp. 785–796, 2017.
C. Granger, Investigating causal relations by econometric models and cross-spectral methods, Econometrica, vol. 37(3), pp. 424–438, 1969.
R. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, OTexts, Melbourne, Australia, 2018.
V. Kotu and B. Deshpande, Data Science: Concepts and Practice, Morgan Kaufmann, Cambridge, Massachusetts, 2019.
J. Lee, Introduction to Topological Manifolds, Springer-Verlag, NewYork, 2011.
E. Lorenz, Deterministic nonperiodic flow, Journal of the Atmospheric Sciences, vol. 20(2), pp. 130–141, 1963.
National Institute of Standards and Technology, Introduction to time series analysis, in NIST/SEMATECH e-Handbook of Statistical Methods, Gaithersburg, Maryland (www.itl.nist.gov/div898/handbook/pmc/section4/pmc4.htm), 2012.
N. Rennie, Empirical Dynamic Models: A Method for Detecting Causality in Complex Deterministic Systems (docplayer.net/156 079632-Empirical-dynamic-models-a-method-for-detecting-causality-in-complex-deterministic-systems.html), 2018.
B. Stone, Enabling Auditing and Intrusion Detection of Proprietary Controller Area Networks, Ph.D. Dissertation, Department of Computer Science, Air Force Institute of Technology, Wright-PattersonAir Force Base, Ohio, 2018.
G. Sugihara, Nonlinear forecasting for the classification of natural time series, Philosophical Transactions of the Royal Society of London, Series A: Physical and Engineering Sciences, vol. 348(1688), pp. 477–495, 1994.
G. Sugihara and R. May, Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series, Nature, vol. 344(6268), pp. 734–741, 1990.
G. Sugihara, R. May, H. Ye, C. Hsieh, E. Deyle, M. Fogarty and S. Munch, Detecting causality in complex ecosystems, Science, vol. 338(6106), pp. 496–500, 2012.
Sugihara Lab, Empirical Dynamic Modeling, Scripps Institution of Oceanography, University of California at San Diego, La Jolla, California (deepecoweb.ucsd.edu/nonlinear-dynamics-research/edm), 2020.
F. Takens, Detecting strange attractors in turbulence, in Dynamical Systems and Turbulence, D. Rand and L. Young (Eds.), Springer, Berlin Heidelberg, Germany, pp. 366–381, 1981.
H. Whitney, Dierentiable manifolds in Euclidean spaces, Proceedings of the National Academy of Sciences, vol. 21(7), pp. 462–464, 1935.
H. Ye, Using rEDM to Quantify Time Delays in Causation (ha0ye.github.io/rEDM/articles/rEDM-time-delay-ccm.html), 2019.
H. Ye, A. Clark, E. Deyle and G. Sugihara, rEDM: An R Package for Empirical Dynamic Modeling and Convergent Cross-Mapping (ha0ye.github.io/rEDM/articles/rEDM.html), 2019.
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Crow, D., Graham, S., Borghetti, B., Sweeney, P. (2020). Engaging Empirical Dynamic Modeling to Detect Intrusions in Cyber-Physical Systems. In: Staggs, J., Shenoi, S. (eds) Critical Infrastructure Protection XIV. ICCIP 2020. IFIP Advances in Information and Communication Technology, vol 596. Springer, Cham. https://doi.org/10.1007/978-3-030-62840-6_6
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DOI: https://doi.org/10.1007/978-3-030-62840-6_6
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