Combining Bayesian Networks and Fishbone Diagrams to Distinguish between Intentional Attacks and Accidental Technical Failures

Saba Chockalingam; Wolter Pieters; Andre M. H. Teixeira; N. Khakzad; Pieter van Gelder

doi:10.1007/978-3-030-15465-3_3

Combining Bayesian Networks and Fishbone Diagrams to Distinguish between Intentional Attacks and Accidental Technical Failures

Saba Chockalingam, Wolter Pieters, Andre M. H. Teixeira, N. Khakzad, Pieter van Gelder

Safety and Security Science

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

5 Citations (Scopus)

585 Downloads (Pure)

Abstract

Because of modern societies' dependence on industrial control systems, adequate response to system failures is essential. In order to take appropriate measures, it is crucial for operators to be able to distinguish between intentional attacks and accidental technical failures. However, adequate decision support for this matter is lacking. In this paper, we use Bayesian Networks (BNs) to distinguish between intentional attacks and accidental technical failures, based on contributory factors and observations (or test results). To facilitate knowledge elicitation, we use extended fishbone diagrams for discussions with experts, and then translate those into the BN formalism. We demonstrate the methodology using an example in a case study from the water management domain.

Original language	English
Title of host publication	Graphical Models for Security - 5th International Workshop, GraMSec 2018, Revised Selected Papers
Subtitle of host publication	Graphical Models for Security
Editors	David Pym, Barbara Fila, George Cybenko
Publisher	Springer
Pages	31-50
Number of pages	20
ISBN (Electronic)	978-3-030-15465-3
ISBN (Print)	978-3-030-15464-6
DOIs	https://doi.org/10.1007/978-3-030-15465-3_3
Publication status	Published - 2019
Event	The Fifth International Workshop on Graphical Models for Security - Mathematical Institute, University of Oxford, Oxford, United Kingdom Duration: 8 Jul 2018 → 8 Jul 2018 http://gramsec.uni.lu/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11086 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Workshop

Workshop	The Fifth International Workshop on Graphical Models for Security
Abbreviated title	GraMSec
Country/Territory	United Kingdom
City	Oxford
Period	8/07/18 → 8/07/18
Internet address	http://gramsec.uni.lu/

Keywords

Bayesian Network
Fishbone Diagram
Intentional Attack
Safety
Security
Technical Failure

Access to Document

10.1007/978-3-030-15465-3_3

Combining Bayesian Networks and Fishbone Diagrams to Distinguish between Intentional Attacks and Accidental Technical FailuresAccepted author manuscript, 857 KB

Cite this

Chockalingam, S., Pieters, W., Teixeira, A. M. H., Khakzad, N., & van Gelder, P. (2019). Combining Bayesian Networks and Fishbone Diagrams to Distinguish between Intentional Attacks and Accidental Technical Failures. In D. Pym, B. Fila, & G. Cybenko (Eds.), Graphical Models for Security - 5th International Workshop, GraMSec 2018, Revised Selected Papers: Graphical Models for Security (pp. 31-50). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11086 LNCS). Springer. https://doi.org/10.1007/978-3-030-15465-3_3

Chockalingam, Saba ; Pieters, Wolter ; Teixeira, Andre M. H. et al. / Combining Bayesian Networks and Fishbone Diagrams to Distinguish between Intentional Attacks and Accidental Technical Failures. Graphical Models for Security - 5th International Workshop, GraMSec 2018, Revised Selected Papers: Graphical Models for Security. editor / David Pym ; Barbara Fila ; George Cybenko. Springer, 2019. pp. 31-50 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Combining Bayesian Networks and Fishbone Diagrams to Distinguish between Intentional Attacks and Accidental Technical Failures",

abstract = "Because of modern societies' dependence on industrial control systems, adequate response to system failures is essential. In order to take appropriate measures, it is crucial for operators to be able to distinguish between intentional attacks and accidental technical failures. However, adequate decision support for this matter is lacking. In this paper, we use Bayesian Networks (BNs) to distinguish between intentional attacks and accidental technical failures, based on contributory factors and observations (or test results). To facilitate knowledge elicitation, we use extended fishbone diagrams for discussions with experts, and then translate those into the BN formalism. We demonstrate the methodology using an example in a case study from the water management domain.",

keywords = "Bayesian Network, Fishbone Diagram, Intentional Attack, Safety, Security, Technical Failure",

author = "Saba Chockalingam and Wolter Pieters and Teixeira, {Andre M. H.} and N. Khakzad and {van Gelder}, Pieter",

year = "2019",

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editor = "David Pym and Barbara Fila and George Cybenko",

booktitle = "Graphical Models for Security - 5th International Workshop, GraMSec 2018, Revised Selected Papers",

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Chockalingam, S, Pieters, W, Teixeira, AMH, Khakzad, N & van Gelder, P 2019, Combining Bayesian Networks and Fishbone Diagrams to Distinguish between Intentional Attacks and Accidental Technical Failures. in D Pym, B Fila & G Cybenko (eds), Graphical Models for Security - 5th International Workshop, GraMSec 2018, Revised Selected Papers: Graphical Models for Security. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11086 LNCS, Springer, pp. 31-50, The Fifth International Workshop on Graphical Models for Security, Oxford, United Kingdom, 8/07/18. https://doi.org/10.1007/978-3-030-15465-3_3

Combining Bayesian Networks and Fishbone Diagrams to Distinguish between Intentional Attacks and Accidental Technical Failures. / Chockalingam, Saba; Pieters, Wolter; Teixeira, Andre M. H. et al.
Graphical Models for Security - 5th International Workshop, GraMSec 2018, Revised Selected Papers: Graphical Models for Security. ed. / David Pym; Barbara Fila; George Cybenko. Springer, 2019. p. 31-50 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11086 LNCS).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Combining Bayesian Networks and Fishbone Diagrams to Distinguish between Intentional Attacks and Accidental Technical Failures

AU - Chockalingam, Saba

AU - Pieters, Wolter

AU - Teixeira, Andre M. H.

AU - Khakzad, N.

AU - van Gelder, Pieter

PY - 2019

Y1 - 2019

N2 - Because of modern societies' dependence on industrial control systems, adequate response to system failures is essential. In order to take appropriate measures, it is crucial for operators to be able to distinguish between intentional attacks and accidental technical failures. However, adequate decision support for this matter is lacking. In this paper, we use Bayesian Networks (BNs) to distinguish between intentional attacks and accidental technical failures, based on contributory factors and observations (or test results). To facilitate knowledge elicitation, we use extended fishbone diagrams for discussions with experts, and then translate those into the BN formalism. We demonstrate the methodology using an example in a case study from the water management domain.

AB - Because of modern societies' dependence on industrial control systems, adequate response to system failures is essential. In order to take appropriate measures, it is crucial for operators to be able to distinguish between intentional attacks and accidental technical failures. However, adequate decision support for this matter is lacking. In this paper, we use Bayesian Networks (BNs) to distinguish between intentional attacks and accidental technical failures, based on contributory factors and observations (or test results). To facilitate knowledge elicitation, we use extended fishbone diagrams for discussions with experts, and then translate those into the BN formalism. We demonstrate the methodology using an example in a case study from the water management domain.

KW - Bayesian Network

KW - Fishbone Diagram

KW - Intentional Attack

KW - Safety

KW - Security

KW - Technical Failure

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U2 - 10.1007/978-3-030-15465-3_3

DO - 10.1007/978-3-030-15465-3_3

M3 - Conference contribution

SN - 978-3-030-15464-6

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 31

EP - 50

BT - Graphical Models for Security - 5th International Workshop, GraMSec 2018, Revised Selected Papers

A2 - Pym, David

A2 - Fila, Barbara

A2 - Cybenko, George

PB - Springer

T2 - The Fifth International Workshop on Graphical Models for Security

Y2 - 8 July 2018 through 8 July 2018

ER -

Chockalingam S, Pieters W, Teixeira AMH, Khakzad N, van Gelder P. Combining Bayesian Networks and Fishbone Diagrams to Distinguish between Intentional Attacks and Accidental Technical Failures. In Pym D, Fila B, Cybenko G, editors, Graphical Models for Security - 5th International Workshop, GraMSec 2018, Revised Selected Papers: Graphical Models for Security. Springer. 2019. p. 31-50. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-15465-3_3

Combining Bayesian Networks and Fishbone Diagrams to Distinguish between Intentional Attacks and Accidental Technical Failures

Abstract

Publication series

Workshop

Keywords

Access to Document

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Fingerprint

Cybersecurity (TPM)

Cite this

Combining Bayesian Networks and Fishbone Diagrams to Distinguish between Intentional Attacks and Accidental Technical Failures

Abstract

Publication series

Workshop

Keywords

Access to Document

Other files and links

Fingerprint

Projects

Cybersecurity (TPM)

Cite this