Page 118 - KDU INTERNATIONAL JOURNAL OF CRIMINAL JUSTICE

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

network, analysts can simulate the impact of different interventions or security
measures and assess their effectiveness in reducing the likelihood or severity of

insider threats or espionage incidents. This allows organizations to make
informed decisions and implement targeted preventive measures.

The Bayesian network modeling provides a robust framework for insider threat
and espionage detection. By capturing uncertainties, updating probabilities, and
considering the interdependencies among various variables, it offers a powerful

approach to understanding, predicting, and mitigating potential risks. The
flexibility and adaptability of Bayesian networks make them valuable tools for

enhancing the security posture of organizations and countering insider threats
and espionage activities.

Challenges and Considerations in implementing Bayesian Theorem

Implementing Bayes' theorem in the context of behavioural mapping of insider
threats and espionage comes with its own set of challenges and considerations.
While Bayes' theorem offers a powerful framework for probabilistic reasoning

and threat detection, several factors must be taken into account to ensure its
effective implementation. Here are some key challenges and considerations.

1. Data Quality and Availability: The accuracy and reliability of Bayesian analysis
heavily rely on the quality and availability of data. Obtaining comprehensive,

representative, and relevant data on user behaviours, access logs,
communication patterns, and other relevant variables can be challenging.

Incomplete or inaccurate data may introduce biases or hinder the accuracy of
the results.

2. Data Preprocessing and Feature Selection: Preprocessing and selecting
relevant features from the collected data are crucial steps in implementing

Bayes' theorem. Cleaning the data, handling missing values, and transforming

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