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Research Papers

A Framework for Development of Risk-Informed Autonomous Adaptive Cyber Controllers

[+] Author and Article Information
Arun Veeramany

Mem. ASME
Pacific Northwest National Laboratory,
902 Battelle Boulevard,
Richland, WA 99352
e-mail: arun.veeramany@pnnl.gov

William J. Hutton

Pacific Northwest National Laboratory,
902 Battelle Boulevard,
Richland, WA 99352
e-mail: william.hutton@pnnl.gov

Siddharth Sridhar

Pacific Northwest National Laboratory,
902 Battelle Boulevard,
Richland, WA 99352
e-mail: siddharth.sridhar@pnnl.gov

Sri Nikhil Gupta Gourisetti

Pacific Northwest National Laboratory,
902 Battelle Boulevard,
Richland, WA 99352
e-mail: srinikhil.gourisetti@pnnl.gov

Garill A. Coles

Pacific Northwest National Laboratory,
902 Battelle Boulevard,
Richland, WA 99352
e-mail: garill.coles@pnnl.gov

Paul M. Skare

Pacific Northwest National Laboratory,
902 Battelle Boulevard,
Richland, WA 99352
e-mail: paul.skare@pnnl.gov

1Corresponding author.

Manuscript received March 8, 2018; final manuscript received February 6, 2019; published online June 3, 2019. Assoc. Editor: Mahesh Mani. The United States Government retains, and by accepting the article for publication, the publisher acknowledges that the United States Government retains, a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for United States government purposes.

J. Comput. Inf. Sci. Eng 19(4), 041004 (Jun 03, 2019) (10 pages) Paper No: JCISE-18-1059; doi: 10.1115/1.4043040 History: Received March 08, 2018; Accepted February 12, 2019

This article details a framework and methodology to risk-inform the decisions of an unsupervised cyber controller. A risk assessment methodology within this framework uses a combination of fault trees, event trees, and attack graphs to trace and map cyber elements with business processes. The methodology attempts to prevent and mitigate cyberattacks by using adaptive controllers that proactively reconfigure a network based on actionable risk estimates. The estimates are based on vulnerabilities and potential business consequences. A generic enterprise-control system is used to demonstrate the wide applicability of the methodology. In addition, data needs, implementation, and potential pitfalls are discussed.

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Figures

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Fig. 1

A framework for risk-informed autonomous adaptive controller

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Fig. 2

A methodology to trace cyberattacks through an enterprise-control system

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Fig. 3

Event tree illustrating possible attack scenarios penetrating through an enterprise-control system

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Fig. 4

Fault tree representation for enterprise level failure

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Fig. 5

Fault tree representation for operations level failure

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Fig. 6

Fault tree representation for control center level failure

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Fig. 7

Fault tree representation for process level failure

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Fig. 8

Fault tree representation for remote station level failure

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Fig. 9

Attack graph identifies cybersecurity linkages to business functions (fault tree nodes)

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