Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
This paper introduces a Petri net model for the propagation of corrupted data in networked systems, including cyber-physical systems. The model abstracts data storage, processing, and communication of a system to estimate the rate and extent of propagation of corrupted data. This facilitates simulation and analysis of the effects of data corruption without requiring a domain-specific simulator. We illustrate the approach by applying it to an IEEE 57-bus smart grid system and analyzing the rate and extent of data corruption - measures of system survivability. The proposed model, with its potential to enable decision support crucial to the sustainability of critical infrastructures, also plays a vital role in shedding light on the propagation of corrupted data and the potential failures that result, providing valuable insights for system design and sustainable operation.
Recommended Citation
Woodard, Mark; Sedigh Sarvestani, Sahra; and Hurson, Ali, "A Petri-net Model for Propagation of Corrupted Data in a Networked System" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 2.
https://aisel.aisnet.org/hicss-58/da/analytics_for_green_is/2
A Petri-net Model for Propagation of Corrupted Data in a Networked System
Hilton Waikoloa Village, Hawaii
This paper introduces a Petri net model for the propagation of corrupted data in networked systems, including cyber-physical systems. The model abstracts data storage, processing, and communication of a system to estimate the rate and extent of propagation of corrupted data. This facilitates simulation and analysis of the effects of data corruption without requiring a domain-specific simulator. We illustrate the approach by applying it to an IEEE 57-bus smart grid system and analyzing the rate and extent of data corruption - measures of system survivability. The proposed model, with its potential to enable decision support crucial to the sustainability of critical infrastructures, also plays a vital role in shedding light on the propagation of corrupted data and the potential failures that result, providing valuable insights for system design and sustainable operation.
https://aisel.aisnet.org/hicss-58/da/analytics_for_green_is/2