Bayesian Network Anomaly Detection

Bayesian networks model the probabilistic relationships between variables. Anomalies can be detected by identifying instances where observed data significantly deviates from the expected probabilities.
The bnlearn package is commonly used for Bayesian network modeling.

R

# Load the package
install.packages("bnlearn")
library(bnlearn)
 
# Generate some example data
set.seed(123)
data <- data.frame(
  A = rnorm(100),
  B = rnorm(100),
  C = rnorm(100)
)
 
# Define the structure of the Bayesian network
# For example, A and B are parents of C
network_structure <- model2network("[A][B][C|A:B]")
 
# Build a Bayesian network
bn_model <- bn.fit(network_structure, data)
print(bn_model)

Output:

  Bayesian network parameters
  Parameters of node A (Gaussian distribution)
Conditional density: A
Coefficients:
(Intercept)  
 0.09040591  
Standard deviation of the residuals: 0.9128159 
  Parameters of node B (Gaussian distribution)
Conditional density: B
Coefficients:
(Intercept)  
 -0.1075468  
Standard deviation of the residuals: 0.9669866 
  Parameters of node C (Gaussian distribution)
Conditional density: C | A + B
Coefficients:
(Intercept)            A            B  
 0.13506543  -0.13317153   0.02381129  
Standard deviation of the residuals: 0.9512979

Anomaly Detection Using R

Anomaly detection is a critical aspect of data analysis, allowing us to identify unusual patterns, outliers, or abnormalities within datasets. It plays a pivotal role across various domains such as finance, cybersecurity, healthcare, and more.

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