  Active Malware Analysis involves modeling malware behavior by executing
actions to trigger responses and explore multiple execution paths. One of the
aims is making the action selection more efficient. This paper treats Active
Malware Analysis as a Bayes-Active Markov Decision Process and uses a Bayesian
Model Combination approach to train an analyzer agent. We show an improvement
in performance against other Bayesian and stochastic approaches to Active
Malware Analysis.
