Abstract
The objective of this work was to study how to assess trustworthiness of computer systems and software used in cyberphysical applications, by developing a multifaceted assessment method and building models based on theories, which rely on non-probabilistic methods and have not been used for such purposes before: (1) Bayesian belief networks, (2) rough sets, (3) Dempster–Shafer theory of evidence, and (4) particle filters. The project objective was accomplished by assuming that trustworthiness is related to the confidence in the results with which related system parameters are evaluated, and then addressing the issue from the perspective of each of the four theories, using a computational model of a cyberphysical system and applying these theories in simple case studies, which can be extended for use in practice.
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References
Zalewski, J., Drager, S., McKeever, W., Kornecki, A.: Measuring security: a challenge for the generation. Ann. Comput. Sci. Inf. Syst. 3, 131–140 (2014)
Kornecki, A., Zalewski, J.: Aviation software: safety and security. In: Webster, J. (ed.) Wiley Encyclopedia of Electrical and Electronics Engineering. Wiley (2015)
Boland, T., Cleraux, C., Fong, E.: Toward a preliminary framework for assisting the trustworthiness of software, report NIST I.A. 7755, National Institute of Standards and Technology, Gaithersburg, November 2010
PAS 754:2014 Software Trustworthiness – Governance and Management – Specification. British Standards Institution, June 2014
Bianco, V., et al.: A survey on open source software trustworthiness. IEEE Softw. 28(5), 67–75 (2011)
Ross, R., McEvilley, M., Oren, J.C.: Systems security engineering: considerations for multidisciplinary approach in the engineering of trustworthy secure systems, NIST S.P. 800-160 – Second Public Draft, National Institute of Standards and Technology, Gaithersburg, May 2016
Pawlak, Z.: Rough sets. Int. J. Comput. Inform. Sci. 11(5), 341–356 (1982)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo (1988)
Jensen, F.V.: An Introduction to Bayesian Networks. Springer, Heidelberg (1996). doi:10.1007/978-3-540-85066-3
Dempster, A.P.: A generalization of Bayesian inference. J. R. Stat. Soc. Ser. B 30, 205–247 (1968)
Doucet, A., Johansen, A.M.: A tutorial on particle filtering and smoothing: fifteen years later. In: Crisan, D., Rozovsky, B. (eds.) The Oxford Handbook of Nonlinear Filtering, pp. 656–704. Oxford University Press, Oxford (2011)
Kornecki, A.J., Wierzchon, S.T., Zalewski, J.: Reasoning under uncertainty with Bayesian belief networks enhanced with rough sets. Int. J. Comput. 12(1), 16–31 (2013)
Rakowsky, U.K.: Fundamentals of the Dempster-Shafer theory and its applications to system safety and reliability modelling. Reliab. Theor. Appl. 2(3–4), 173–185 (2007)
Al-Saleh, M.F., Masoud, F.A.: A note on the posterior expected loss as a measure of accuracy in Bayesian methods. Appl. Math. Comput. 134, 507–514 (2003)
Reineking, T.: Particle filtering in the Dempster-Shafer theory. Int. J. Approx. Reason. 52, 1124–1135 (2011)
Acknowledgment
This project was supported in part by the AFOSR 2016 Summer Faculty Fellowship Program at Rome Labs. Approved for public release, Case Number 88ABW-2017-1078. Distribution unlimited.
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Drager, S., Zalewski, J. (2017). A Novel and Unifying View of Trustworthiness in Cyberphysical Systems. In: Rak, J., Bay, J., Kotenko, I., Popyack, L., Skormin, V., Szczypiorski, K. (eds) Computer Network Security. MMM-ACNS 2017. Lecture Notes in Computer Science(), vol 10446. Springer, Cham. https://doi.org/10.1007/978-3-319-65127-9_26
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