Page 122 - KDU INTERNATIONAL JOURNAL OF CRIMINAL JUSTICE

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

Various cases of Chinese espionage, involving APT groups, highlight the need for
advanced threat detection techniques. Bayesian modeling can help identify

suspicious behaviors and anomalies associated with these threats. By combining
historical data, external threat intelligence, and Bayesian analysis, security
analysts can assess the likelihood of specific individuals or groups being

involved in insider threat activities or espionage.

5. Insider Trading in Financial Institutions

Insider trading involves individuals using non-public information to gain an

unfair advantage in trading stocks or securities. Bayesian networks can be
employed to map the behaviours of employees within financial institutions,

including access to sensitive information, trading activities, and communication
patterns. By updating probabilities based on observed activities and detecting
suspicious patterns, Bayesian theorem can help identify potential insider

trading incidents and trigger timely investigations.

6. Espionage Detection in Government Agencies

Government agencies are often targeted by foreign intelligence services seeking
to gather sensitive information. Bayesian networks can be applied to analyze

data from multiple sources, including communication logs, access logs, and
employee profiles, to identify potential espionage activities. By incorporating

variables such as unusual communication patterns, access to classified
information, and social network connections, Bayesian networks can help assess

the likelihood of espionage threats and prioritize investigative efforts.

7.Fraud Detection in Corporate Environments

Insider fraud can have significant financial implications for organizations.
Bayesian networks can aid in detecting fraudulent activities by modeling

variables such as employee behaviours, access privileges, financial transactions,

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