  Insider threats are costly, hard to detect, and unfortunately rising in
occurrence. Seeking to improve detection of such threats, we develop novel
techniques to enable us to extract powerful features and augment attack vectors
for greater classification power. Most importantly, we generate high quality
color image encodings of user behavior that do not have the downsides of
traditional greyscale image encodings. Combined, they form Computer Vision User
and Entity Behavior Analytics, a detection system designed from the ground up
to improve upon advancements in academia and mitigate the issues that prevent
the usage of advanced models in industry. The proposed system beats
state-of-art methods used in academia and as well as in industry on a gold
standard benchmarking dataset.
