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Evidential Network Modeling for Cyber-Physical System State Inference.

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Evidential Network Modeling for Cyber-Physical System State Inference. / Friedberg, Ivo; Hong, Xin; McLaughlin, Kieran et al.
In: IEEE Access, Vol. 5, 31.12.2017, p. 17149-17164.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Friedberg, I, Hong, X, McLaughlin, K, Smith, P & Miller, PC 2017, 'Evidential Network Modeling for Cyber-Physical System State Inference.', IEEE Access, vol. 5, pp. 17149-17164. https://doi.org/10.1109/ACCESS.2017.2718498

APA

Friedberg, I., Hong, X., McLaughlin, K., Smith, P., & Miller, P. C. (2017). Evidential Network Modeling for Cyber-Physical System State Inference. IEEE Access, 5, 17149-17164. https://doi.org/10.1109/ACCESS.2017.2718498

Vancouver

Friedberg I, Hong X, McLaughlin K, Smith P, Miller PC. Evidential Network Modeling for Cyber-Physical System State Inference. IEEE Access. 2017 Dec 31;5:17149-17164. Epub 2017 Jun 22. doi: 10.1109/ACCESS.2017.2718498

Author

Friedberg, Ivo ; Hong, Xin ; McLaughlin, Kieran et al. / Evidential Network Modeling for Cyber-Physical System State Inference. In: IEEE Access. 2017 ; Vol. 5. pp. 17149-17164.

Bibtex

@article{78851a81ce3a4b268dfa045564a7a113,
title = "Evidential Network Modeling for Cyber-Physical System State Inference.",
abstract = "Cyber-physical systems (CPSs) have dependability requirements that are associated with controlling a physical process. Cyber-attacks can result in those requirements not being met. Consequently, it is important to monitor a CPS in order to identify deviations from normal operation. A major challenge is inferring the cause of these deviations in a trustworthy manner. This is necessary to support the implementation of correct and timely control decisions, in order to mitigate cyber-attacks and other causes of reduced dependability. This paper presents evidential networks as a solution to this problem. Through the evaluation of a representative use case for cyber-physical control systems, this paper shows novel approaches to integrate low-level sensors of different types, in particular those for cyber-attack detection, and reliabilities into evidential networks. The results presented indicate that evidential networks can identify system states with an accuracy that is comparable to approaches that use classical Bayesian probabilities to describe causality. However, in addition, evidential networks provide information about the uncertainty of a derived system state, which is a significant benefit, as it can be used to build trust in the results of automatic reasoning systems.",
author = "Ivo Friedberg and Xin Hong and Kieran McLaughlin and Paul Smith and Miller, {Paul C.}",
year = "2017",
month = dec,
day = "31",
doi = "10.1109/ACCESS.2017.2718498",
language = "English",
volume = "5",
pages = "17149--17164",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS

TY - JOUR

T1 - Evidential Network Modeling for Cyber-Physical System State Inference.

AU - Friedberg, Ivo

AU - Hong, Xin

AU - McLaughlin, Kieran

AU - Smith, Paul

AU - Miller, Paul C.

PY - 2017/12/31

Y1 - 2017/12/31

N2 - Cyber-physical systems (CPSs) have dependability requirements that are associated with controlling a physical process. Cyber-attacks can result in those requirements not being met. Consequently, it is important to monitor a CPS in order to identify deviations from normal operation. A major challenge is inferring the cause of these deviations in a trustworthy manner. This is necessary to support the implementation of correct and timely control decisions, in order to mitigate cyber-attacks and other causes of reduced dependability. This paper presents evidential networks as a solution to this problem. Through the evaluation of a representative use case for cyber-physical control systems, this paper shows novel approaches to integrate low-level sensors of different types, in particular those for cyber-attack detection, and reliabilities into evidential networks. The results presented indicate that evidential networks can identify system states with an accuracy that is comparable to approaches that use classical Bayesian probabilities to describe causality. However, in addition, evidential networks provide information about the uncertainty of a derived system state, which is a significant benefit, as it can be used to build trust in the results of automatic reasoning systems.

AB - Cyber-physical systems (CPSs) have dependability requirements that are associated with controlling a physical process. Cyber-attacks can result in those requirements not being met. Consequently, it is important to monitor a CPS in order to identify deviations from normal operation. A major challenge is inferring the cause of these deviations in a trustworthy manner. This is necessary to support the implementation of correct and timely control decisions, in order to mitigate cyber-attacks and other causes of reduced dependability. This paper presents evidential networks as a solution to this problem. Through the evaluation of a representative use case for cyber-physical control systems, this paper shows novel approaches to integrate low-level sensors of different types, in particular those for cyber-attack detection, and reliabilities into evidential networks. The results presented indicate that evidential networks can identify system states with an accuracy that is comparable to approaches that use classical Bayesian probabilities to describe causality. However, in addition, evidential networks provide information about the uncertainty of a derived system state, which is a significant benefit, as it can be used to build trust in the results of automatic reasoning systems.

U2 - 10.1109/ACCESS.2017.2718498

DO - 10.1109/ACCESS.2017.2718498

M3 - Journal article

VL - 5

SP - 17149

EP - 17164

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -