Year: 2021
Type: preprint
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.
Source: arXiv (Cornell University)
Author: Khanna, Sameer
Institutions: Fortinet (United States), Stanford University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4289539415