  Better methods to detect insider threats need new anticipatory analytics to
capture risky behavior prior to losing data. In search of the best overall
classifier, this work empirically scores 88 machine learning algorithms in 16
major families. We extract risk features from the large CERT dataset, which
blends real network behavior with individual threat narratives. We discover the
predictive importance of measuring employee sentiment. Among major classifier
families tested on CERT, the random forest algorithms offer the best choice,
with different implementations scoring over 98% accurate. In contrast to more
obscure or black-box alternatives, random forests are ensembles of many
decision trees and thus offer a deep but human-readable set of detection rules
(>2000 rules). We address performance rankings by penalizing long execution
times against higher median accuracies using cross-fold validation. We address
the relative rarity of threats as a case of low signal-to-noise (< 0.02%
malicious to benign activities), and then train on both under-sampled and
over-sampled data which is statistically balanced to identify nefarious actors.
