Using Color To Identify Insider Threats

TL;DR

This paper introduces a novel color image encoding technique for user behavior analysis that enhances insider threat detection, outperforming existing methods on benchmark datasets.

Contribution

It develops a new color encoding method for user behavior data and a detection system that surpasses current state-of-the-art techniques in insider threat identification.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Introduces high-quality color image encodings for behavior analysis

Enhances detection accuracy of insider threats

Abstract

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.