Bayesian Evaluation of Trojan Horse Forensic Evidence
Organized by: International Forensic Scientist Awards
Website: forensicscientist.org
16th Edition of Forensic Scientist Awards 28-29 November 2025 | Agra, India
In cybercrime investigations, the Trojan horse defense often challenges digital forensic experts to determine whether malicious activity was intentional or the result of hidden malware. Evaluating such claims requires more than technical examination—it demands a probabilistic approach that accounts for uncertainty and interrelated digital evidence.
This study presents the use of Bayesian networks as a structured framework for assessing forensic findings in Trojan horse defense cases. By modeling causal relationships between user actions, malware behavior, and system evidence, Bayesian analysis provides a rational, transparent method for weighing competing hypotheses.
Unlike traditional deterministic methods, Bayesian reasoning quantifies uncertainty and allows dynamic updating of probabilities as new evidence emerges. This ensures a more balanced interpretation of digital traces, improving the reliability of expert conclusions presented in court.
Ultimately, integrating Bayesian networks into digital forensic analysis strengthens the objectivity, consistency, and credibility of findings—supporting justice in complex cybercrime cases where intent and evidence are often blurred.
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