What we know
The work proposes a decision support system based on a Bayesian belief network to guide cyber deception strategies from a detailed model of attacker beliefs, illustrated with a case study using passively observed operating system fingerprinting data. The authors compare the model's prediction accuracy with a sequential artificial neural network and introduce a deception algorithm to select a minimal set of features whose modification would make a system appear as a different operating system.
Evidence limits
Review limited to the Crossref deposited abstract; the full text was not consulted and results were not independently validated.
Reviewed: author abstract. Full-text results were not analyzed.
Sources and provenance
- Bayesian Models for Targeted Cyber Deception Strategies (Student Abstract)
abstract · 2026-10-11
Reviewed: 2026-10-16. This record may change when new evidence is found.