[PPT] - An Ontological Approach for Generating Useful Discrete-Event Dynamic PowerPoint Presentation

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An Ontological Approach for Generating Useful Discrete-Event Dynamic System Models

Ken Keefe

PhD 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 Implementation 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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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 Systems

Generalization [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 Systems

Generalization

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 Systems

Generalization

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 Systems

Generalization

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 Systems

Generalization

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

f 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

f 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

f 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 controls Power Device Controlled Power Dev controls Data Base Ontology MG Ontology Device Software 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 State Variable

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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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