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Proceedings Paper

A vector relational data modeling approach to Insider threat intelligence
Author(s): Ryan F. Kelly; Thomas S. Anderson
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Paper Abstract

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.

Paper Details

Date Published: 12 May 2016
PDF: 10 pages
Proc. SPIE 9831, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII, 98310W (12 May 2016); doi: 10.1117/12.2224299
Show Author Affiliations
Ryan F. Kelly, Naval Postgraduate School (United States)
Thomas S. Anderson, Naval Postgraduate School (United States)

Published in SPIE Proceedings Vol. 9831:
Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII
Michael A. Kolodny; Tien Pham, Editor(s)

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