Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ACCESS.2017.2718498 | IEEE ACCESS |
Keywords | Field | DocType |
Communication technologies,computer networks,network security,data system,data processing,data integration,industry applications,security,security management,power engineering and energy,power systems,microgrid,attack causality | Data mining,Causality,Dependability,Computer science,Trustworthiness,Inference,Cyber-physical system,Artificial intelligence,Control system,Network model,Machine learning,Bayesian probability | Journal |
Volume | ISSN | Citations |
5 | 2169-3536 | 1 |
PageRank | References | Authors |
0.35 | 17 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ivo Friedberg | 1 | 40 | 2.75 |
| Xin Hong | 2 | 7 | 2.13 |
| Kieran McLaughlin | 3 | 208 | 22.19 |
| Paul Smith | 4 | 94 | 10.97 |
| Paul Miller | 5 | 141 | 9.83 |