An Ontological Approach for Generating Useful Discrete

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

Ken KeefePhD Qualifying Examination - 2020

▪ 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

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

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.”

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.”

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.”

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.”

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.”

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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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