an ontological approach for generating useful discrete
TRANSCRIPT
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An Ontological Approach for Generating Useful Discrete-Event Dynamic System Models
Ken KeefePhD Qualifying Examination - 2020
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▪ Introduction▪ Problem Description▪ Manual Model Development▪ Approach▪ Ontologies and Knowledge Bases▪ Accomplishments▪ Future Work
Talk Overview
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Introduction▪ Understanding complex systems is
extremely challenging
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Introduction▪ Understanding complex systems is
extremely challenging▪ Mathematical models can be an
excellent option– Formally stated assumptions– Repeatable studies– Quantitative metrics– Many problem domains
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Water Construction Power Logistics Transportation Networks
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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– Simulation
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Water Construction Power Logistics Transportation Networks
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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
Problem Description
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Manual Model Development
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Real System
[1] J. Banks, J. Carson, B. Nelson, and D. Nicol, Discrete-Event System Simulation.[2] O. Balci, “Verification, Validation, and Testing.”
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Manual Model Development
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Real System
Conceptual Model
Abstraction
[1] J. Banks, J. Carson, B. Nelson, and D. Nicol, Discrete-Event System Simulation.[2] O. Balci, “Verification, Validation, and Testing.”
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Manual Model Development
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Real System
Conceptual Model
Operational Model
Abstraction
Implementation
[1] J. Banks, J. Carson, B. Nelson, and D. Nicol, Discrete-Event System Simulation.[2] O. Balci, “Verification, Validation, and Testing.”
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Manual Model Development
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Real System
Conceptual Model
Operational Model
Abstraction
ImplementationVerification
[1] J. Banks, J. Carson, B. Nelson, and D. Nicol, Discrete-Event System Simulation.[2] O. Balci, “Verification, Validation, and Testing.”
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Manual Model Development
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Real System
Conceptual Model
Operational Model
Abstraction
Implementation
Validation
Verification
[1] J. Banks, J. Carson, B. Nelson, and D. Nicol, Discrete-Event System Simulation.[2] O. Balci, “Verification, Validation, and Testing.”
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Approach
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Real System Universe of Real SystemsGeneralization
[3] K. Keefe, B. Feddersen, M. Rausch, R. Wright, and W. H. Sanders, “An Ontology Framework for Generating Discrete-Event Stochastic Models,” EPEW 2018.
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Approach
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Real System Universe of Real SystemsGeneralization
Ontology of System Elements
Abstraction
[3] K. Keefe, B. Feddersen, M. Rausch, R. Wright, and W. H. Sanders, “An Ontology Framework for Generating Discrete-Event Stochastic Models,” EPEW 2018.
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Approach
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Real System
Conceptual Model
Abstraction
Universe of Real SystemsGeneralization
Ontology of System Elements
Abstraction
Types
[3] K. Keefe, B. Feddersen, M. Rausch, R. Wright, and W. H. Sanders, “An Ontology Framework for Generating Discrete-Event Stochastic Models,” EPEW 2018.
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Approach
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Real System
Conceptual Model
Operational Model
Abstraction
Implementation
Universe of Real SystemsGeneralization
Ontology of System Elements
Abstraction
Types
Generator
System Spec. Model Fragments
[3] K. Keefe, B. Feddersen, M. Rausch, R. Wright, and W. H. Sanders, “An Ontology Framework for Generating Discrete-Event Stochastic Models,” EPEW 2018.
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Approach
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Real System
Conceptual Model
Operational Model
Abstraction
Implementation
Validation
Verification
Universe of Real SystemsGeneralization
Ontology of System Elements
Abstraction
Types
Generator
System Spec. Model Fragments
Verification
Validation
[3] K. Keefe, B. Feddersen, M. Rausch, R. Wright, and W. H. Sanders, “An Ontology Framework for Generating Discrete-Event Stochastic Models,” EPEW 2018.
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Ontologies and Knowledge Bases
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● 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.
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Case Studies
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[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.
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Case Studies
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[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.
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Case Studies
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[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.
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Microgrid
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[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.
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Controller
Microgrid Controller
Generator Controller
Microgrid Ontology
NG Gen Controller
Relay
Diesel Gen Controller
controlsPower Device
Controlled Power Dev
controlsData
Base Ontology
MG Ontology
DeviceSoftware
managedBy
hardwarePlatform
readsData
Power Line
powerConnection
Transform.
Breaker
ESS
[5]
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Microgrid ADVISE Model Generation
Key
Access
Attack Step
Knowledge
Goal
Skill
System StateVariable
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▪ Validation, Verification, and Testing
▪ Model generation of additional formalisms (SAN, RBD)
Immediate Future Work
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▪ Large, complex model generation– Model decomposition and
interconnection– Reward measure
generation
Future Work▪ Model Granularity
– Ontology representation of levels or spectrum
– Automated granularity selection • Entire model• Model parts
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[1] J. Banks, J. Carson, B. Nelson, and D. Nicol, Discrete-Event System Simulation, 5th ed. Prentice Hall, 2010.
[2] O. Balci, “Verification, Validation, and Testing,” in Handbook of Simulation, J. Banks, Ed. John Wiley & Sons, Ltd, 2007, ch. 10, pp.335–393.
[3] K. Keefe, B. Feddersen, M. Rausch, R. Wright, and W. H. Sanders, “An Ontology Framework for Generating Discrete-Event Stochastic Models,” in Proceedings of the 15th European Performance Engineering Workshop (EPEW 2018), Paris, France, October 2018, pp.173–189.
[4] T. R. Gruber, “A Translation Approach to Portable Ontology Specifications,” Knowledge Acquisition, vol. 5, no. 2, pp. 199-220, 1993.
References[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.
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