  Behavioral Indicators of Compromise are associated with various automated
methods used to extract the sample behavior by observing the system function
calls performed in a virtual execution environment. Thus, every sample is
described by a set of BICs triggered by the sample behavior in the sandbox
environment. Here we discuss a Machine Learning approach to the classification
of the sandbox samples as MALICIOUS or BENIGN, based on the list of triggered
BICs. Besides the more traditional methods like Logistic Regression and Naive
Bayes Classification we also discuss a different approach inspired by the
statistical Monte Carlo methods. The numerical results are illustrated using
ThreatGRID and ReversingLabs data.
