ABSTRACT
A Bayesian network (BN) is a directed acyclic graph with an associated probability distribution function and a graphical probabilistic model for multivariate research. When it comes to cybersecurity, Bayesian networks are the go-to solutions for integrating abuse network detectors and anomaly detectors. The Bayesian network can learn the behavior of the model and forecast its result after a training phase. The brain can be viewed as a Bayesian computer, and perception can be compared to Bayesian reasoning. We research dysfunctions in hierarchical Bayesian reasoning processes, which are thought to underpin cognition and belief fixation in healthy individuals, in the developing discipline of computational psychiatry. In this chapter, we present Bayesian models and their uses in modeling beliefs and behavior in cognitive neuroscience. Illustrative Bayesian network models were developed to compare Bayesian views in cybersecurity. The fact that observer beliefs are represented as probability distributions in Bayesian models, as opposed to other classes of models, is a key characteristic. This enables you to incorporate information while taking uncertainty into consideration. In this chapter, we look at two different problems that the Bayesian approach may be effective for.:optimal synthesis of evidence from many sources and construction of environmental perceptions with limited knowledge (e.g. during learning).
