Probability elicitation for Bayesian networks to distinguish between intentional attacks and accidental technical failures.
An object of class bn.fit
. Refer to the documentation of bnlearn
for details.
A discrete Bayesian network modeling a floodgate in the Netherlands. Probabilities were given within the referenced paper. The vertices are:
Weak physical access-control (True, False);
Sensor data integrity verification (True, False);
Lack of physical maintenance (True, False);
Well-written maintenance procedure (True, False);
Major cause for sensor sends incorrect water level measurements (Intentional Attack, Accidental Technical Failure);
Abnormalities in the other locations (True, False);
Sensor sends correct water level measurements after cleaning the sensor (True, False)
Sensor sends correct water level measurements after recalibrating the sensor (True, False);
Chockalingam, S., Pieters, W., Teixeira, A. M., & van Gelder, P. (2023). Probability elicitation for Bayesian networks to distinguish between intentional attacks and accidental technical failures. Journal of Information Security and Applications, 75, 103497.