An Ontological Approach for Generating Useful Discrete-Event Dynamic System Models Ken Keefe PhD Qualifying Examination - 2020
Talk Overview ▪ Introduction ▪ Problem Description ▪ Manual Model Development ▪ Approach ▪ Ontologies and Knowledge Bases ▪ Accomplishments ▪ Future Work 2 of 14
Introduction ▪ Understanding complex systems is extremely challenging 3 of 14
Introduction ▪ Understanding complex systems is extremely challenging ▪ Mathematical models can be an excellent option – Formally stated assumptions – Repeatable studies – Quantitative metrics – Many problem domains Water Construction Power Logistics Transportation Networks 3 of 14
Introduction ▪ Understanding complex systems is extremely challenging ▪ Mathematical models can be an excellent option – Formally stated assumptions – Repeatable studies – Quantitative metrics – Many problem domains ▪ Discrete-Event Dynamic System (DEDS) Models – Probabilistic – Time – State variables Water Construction Power Logistics Transportation Networks – Simulation 3 of 14
Problem Description DEDS models of complex systems are usually manually developed by human beings. This development process: ▪ Is time-consuming ▪ Requires expertise (modeling, system design, system operation, etc.) ▪ Is error-prone – Poor Assumptions – Inconsistent Models/Submodels – Inappropriate Model Granularity – Incompleteness – Bugs 4 of 14
Manual Model Development Real System [1] J. Banks, J. Carson, B. Nelson, and D. Nicol, Discrete-Event System Simulation . [2] O. Balci, “Verification, Validation, and Testing.” 5 of 14
Manual Model Development Real System Abstraction Conceptual Model [1] J. Banks, J. Carson, B. Nelson, and D. Nicol, Discrete-Event System Simulation . [2] O. Balci, “Verification, Validation, and Testing.” 5 of 14
Manual Model Development Real System Abstraction Conceptual Model Implementation Operational Model [1] J. Banks, J. Carson, B. Nelson, and D. Nicol, Discrete-Event System Simulation . [2] O. Balci, “Verification, Validation, and Testing.” 5 of 14
Manual Model Development Real System Abstraction Conceptual Model Verification Implementation Operational Model [1] J. Banks, J. Carson, B. Nelson, and D. Nicol, Discrete-Event System Simulation . [2] O. Balci, “Verification, Validation, and Testing.” 5 of 14
Manual Model Development Real System Abstraction Validation Conceptual Model Verification Implementation Operational Model [1] J. Banks, J. Carson, B. Nelson, and D. Nicol, Discrete-Event System Simulation . [2] O. Balci, “Verification, Validation, and Testing.” 5 of 14
Approach Universe of Real System Generalization Real Systems [3] K. Keefe , B. Feddersen, M. Rausch, R. Wright, and W. H. Sanders, “An Ontology Framework for Generating Discrete-Event Stochastic Models,” EPEW 2018. 6 of 14
Approach Universe of Real System Generalization Real Systems Abstraction Ontology of System Elements [3] K. Keefe , B. Feddersen, M. Rausch, R. Wright, and W. H. Sanders, “An Ontology Framework for Generating Discrete-Event Stochastic Models,” EPEW 2018. 6 of 14
Approach Universe of Real System Generalization Real Systems Abstraction Abstraction Ontology of Conceptual Model Types System Elements [3] K. Keefe , B. Feddersen, M. Rausch, R. Wright, and W. H. Sanders, “An Ontology Framework for Generating Discrete-Event Stochastic Models,” EPEW 2018. 6 of 14
Approach Universe of Real System Generalization Real Systems Abstraction Abstraction Ontology of Conceptual Model Types System Elements System Spec. Model Fragments Generator Implementation Operational Model [3] K. Keefe , B. Feddersen, M. Rausch, R. Wright, and W. H. Sanders, “An Ontology Framework for Generating Discrete-Event Stochastic Models,” EPEW 2018. 6 of 14
Approach Universe of Real System Generalization Real Systems Abstraction Abstraction Validation Validation Ontology of Conceptual Model Types System Elements System Spec. Model Fragments Generator Verification Verification Implementation Operational Model [3] K. Keefe , B. Feddersen, M. Rausch, R. Wright, and W. H. Sanders, “An Ontology Framework for Generating Discrete-Event Stochastic Models,” EPEW 2018. 6 of 14
Ontologies and Knowledge Bases ● Ontology - A formal definition of types, attributes, and relationships. ● Knowledge Base - A formal statement of data that is organized by an ontology. [4] T. R. Gruber, “A Translation Approach to Portable Ontology Specifications,” Knowledge Acquisition, vol. 5, no. 2, pp. 199-220, 1993. 7 of 14
Case Studies [5] M. Backes, K. Keefe , and A. Valdes, “A Microgrid Ontology for the Analysis of Cyber-Physical Security,” in Proceedings of the 2017 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES) , Pittsburg, Pennsylvania, USA, April 2017, pp. 1–6. [6] M. Rausch, K. Keefe , B. Feddersen, and W. H. Sanders, “Automatically Generating Security Models from System Models to Aid in the Evaluation of AMI Deployment Options,” in Proceedings of the 12th International Conference on Critical Information Infrastructures Security (CRITIS) , Lucca, Italy, October 2017, pp. 156–167. [7] C. Cheh, K. Keefe , B. Feddersen, B. Chen, W. G. Temple, and W. Sanders, “Developing Models for Physical Attacks in Cyber-Physical Systems,” in Proceedings of the Cyber-Physical Systems Security and PrivaCy (CPS-SPC) Workshop , Dallas, Texas, USA, November 2017, pp. 49–55. 8 of 14
Case Studies [5] M. Backes, K. Keefe , and A. Valdes, “A Microgrid Ontology for the Analysis of Cyber-Physical Security,” in Proceedings of the 2017 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES) , Pittsburg, Pennsylvania, USA, April 2017, pp. 1–6. [6] M. Rausch, K. Keefe , B. Feddersen, and W. H. Sanders, “Automatically Generating Security Models from System Models to Aid in the Evaluation of AMI Deployment Options,” in Proceedings of the 12th International Conference on Critical Information Infrastructures Security (CRITIS) , Lucca, Italy, October 2017, pp. 156–167. [7] C. Cheh, K. Keefe , B. Feddersen, B. Chen, W. G. Temple, and W. Sanders, “Developing Models for Physical Attacks in Cyber-Physical Systems,” in Proceedings of the Cyber-Physical Systems Security and PrivaCy (CPS-SPC) Workshop , Dallas, Texas, USA, November 2017, pp. 49–55. 8 of 14
Case Studies [5] M. Backes, K. Keefe , and A. Valdes, “A Microgrid Ontology for the Analysis of Cyber-Physical Security,” in Proceedings of the 2017 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES) , Pittsburg, Pennsylvania, USA, April 2017, pp. 1–6. [6] M. Rausch, K. Keefe , B. Feddersen, and W. H. Sanders, “Automatically Generating Security Models from System Models to Aid in the Evaluation of AMI Deployment Options,” in Proceedings of the 12th International Conference on Critical Information Infrastructures Security (CRITIS) , Lucca, Italy, October 2017, pp. 156–167. [7] C. Cheh, K. Keefe , B. Feddersen, B. Chen, W. G. Temple, and W. Sanders, “Developing Models for Physical Attacks in Cyber-Physical Systems,” in Proceedings of the Cyber-Physical Systems Security and PrivaCy (CPS-SPC) Workshop , Dallas, Texas, USA, November 2017, pp. 49–55. 8 of 14
Microgrid [5] M. Backes, K. Keefe , and A. Valdes, “A Microgrid Ontology for the Analysis of Cyber-Physical Security,” in Proceedings of the 2017 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES) , Pittsburg, Pennsylvania, USA, April 2017, pp. 1–6. 9 of 14
Base Ontology Microgrid Ontology [5] MG Ontology Power Line hardwarePlatform powerConnection Software Device Power Transform. controls Device managedBy readsData controls Data Controller Controlled Breaker Power Dev Microgrid Generator Relay Controller Controller ESS NG Gen Diesel Gen Controller Controller 10 of 14
Microgrid ADVISE Model Generation Key Access Attack Step Knowledge Goal Skill System State Variable 11 of 14
Immediate Future Work ▪ Validation, Verification, and Testing ▪ Model generation of additional formalisms (SAN, RBD) 12 of 14
Future Work ▪ Large, complex model ▪ Model Granularity – Ontology representation of generation levels or spectrum – Model decomposition and – Automated granularity interconnection selection – Reward measure • Entire model generation • Model parts 13 of 14
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