Page 119 - KDU INTERNATIONAL JOURNAL OF CRIMINAL JUSTICE

KDU International Journal of Criminal Justice (KDUIJCJ)
Volume I | Issue II| July 2024

the data into a suitable format for analysis can be complex. Additionally,
selecting the most informative and discriminatory features to capture relevant

behavioral patterns requires domain expertise and careful consideration.

3. Prior Probability Estimation: Assigning prior probabilities to the variables in

the Bayesian network can be challenging, as it often involves making subjective
judgments or relying on historical data that may not fully capture the current
threat landscape. Accurate estimation of prior probabilities is critical to ensure

the initial beliefs are appropriately represented in the model.

4. Updating Probabilities and Evidence Fusion: Updating probabilities based on
new evidence and integrating multiple sources of evidence pose challenges.

Incorporating real-time data, external threat intelligence, or feedback from
security analysts requires efficient methods for evidence fusion and updating
probabilities in a timely manner. Balancing the weight of different sources of

evidence and handling conflicting or uncertain information is essential.

5. Model Complexity and Scalability: Bayesian networks can become

increasingly complex as more variables and dependencies are considered.
Handling large-scale networks with numerous nodes and edges can pose

computational challenges and require efficient algorithms for inference and
parameter learning. Ensuring the scalability of the model is important for
practical implementation.

6. Interpretability and Explainability: Interpreting and explaining the results of

Bayesian analysis to security analysts, investigators, and stakeholders is crucial
for effective decision-making. Communicating complex probabilistic reasoning

in a clear and understandable manner can be challenging, and efforts should be
made to provide insights and actionable information in a user-friendly manner.

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