Lewis, “A Scalable Sampling Method to High-Dimensional Uncertainties for Optimal and Reinforcement Learning-Based Controls,” IEEE Control Systems Letters, vol. Wan, “Similarity search of spatiotemporal scenarios for strategic air traffic management,” in Proceedings of the AIAA Aviation Conference, June 2018. Lewis, “On the Identifiability of the Influence Model for Stochastic Spatiotemporal Spread Processes,” in Proceedings of American Control Conference, Philadelphia, PA, July 10-12, 2019. Lin, “M-PCM-OFFD: An Effective Output Statistics Estimation Method for Systems of High Dimensional Uncertainties Subject to Low-Order Parameter Interactions,” Mathematics and Computers in Simulation, vol. Steiner, “Similarity Search of Spatiotemporal Scenario Data for Strategic Air Traffic Management,” AIAA Journal of Aerospace Information Systems, vol. Wan, “Clustering Stochastic Weather Scenarios using Influence Model-based Distance Measures,” in Proceedings of AIAA Aviation Conference, Dallas, TX, June 17-21, 2019. Xie, “Spatiotemporal Scenario Data-Driven Decision For the Path Planning of Multiple UASs,” to be submitted to AIAA Journal of Aerospace Information Systems. Lewis, “Reinforcement Learning-based Approximate Minimum Time Path Planning of UAVs in Wind Fields,” submitted to American Control Conference, 2019. Jalaian, “Computational Intelligence in Uncertainty Quantification for Learning Control and Differential Games,” accepted, Book Chapter, 2019. Lei, “Effective Uncertainty Evaluation in Large-Scale Systems (book chapter),” Principles of Cyber-Physical Systems, Cambridge University Press, accepted for publication, pp. Lewis, “On the Identifiability of the Influence Model for Stochastic Spatiotemporal Spread Processes,” IEEE Transactions on Systems, Man, and Cybernetics, accepted, April 2019.
#SYNERGY CPS SOFTWARE#
This case study will include development and deployment of software decision aids for managing man-made disturbances to the air traffic system. Three canonical types of threats will be addressed: environmental-to-physical threats, cyber-physical co-threats, and human-in-the-loop threats.
As a central case study, the framework and tools will be used for threat assessment and risk analysis of strategic air traffic management. We will then pursue analyses that tie special infrastructure-network features to security/vulnerability. Specifically, three functionalities termed Target, Feature, and Defend will be developed, which exploit topological characteristics of an MCCPI to evaluate and mitigate threat impacts. The attendant tool suite will provide situational awareness of the propagative impacts of threats.
The proposed modeling framework for MCCPIs has three aspects: 1) a tractable moment-linear modeling paradigm for the hybrid, stochastic, and multi-layer dynamics of MCCPIs 2) models for sentient and natural adversaries, that capture their measurement and actuation capabilities in the cyber- and physical- worlds, intelligence, and trust-level and 3) formal definitions for information security and vulnerability. We propose here to develop a modeling framework and tool suite for threat assessment for MCCPIs. These management-coupled cyber- and physical- infrastructures (MCCPIs) are subject to complex threats from natural and sentient adversaries, which can enact complex propagative impacts across networked physical-, cyber-, and human elements. This changing paradigm is leading to tight coupling of the cyber- infrastructure with multiple physical- world infrastructures, including air transportation and electric power systems. Strategic decision-making for physical-world infrastructures is rapidly transitioning toward a pervasively cyber-enabled paradigm, in which human stakeholders and automation leverage the cyber-infrastructure at large (including on-line data sources, cloud computing, and handheld devices). About the Project CPS: TTP Option: Synergy: Collaborative Research: Threat-Assessment Tools for Management-Coupled Cyber- and Physical- Infrastructure NSF Project Numbers and Links: 1714826, 1544863
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