A vector relational data modeling approach to Insider threat intelligence
Ryan F. Kelly, Thomas S. Anderson · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2016
We address the problem of detecting insider threats before they can do harm. In many cases, co-workers notice indications of suspicious activity prior to insider threat attacks. A partial solution to this problem requires an understanding of how information can better traverse the communication network between human intelligence and insider threat analysts. Our approach employs modern mobile communications technology and scale free network architecture to reduce the network distance between human sensors and analysts. In order to solve this problem, we propose a Vector Relational Data Modeling approach to integrate human “sensors,” geo-location, and existing visual analytics tools. This integration problem is known to be difficult due to quadratic increases in cost associated with complex integration solutions. A scale free network integration approach using vector relational data modeling is proposed as a method for reducing network distance without increasing cost.