A vector relational data modeling approach to Insider threat intelligence

Ryan F. Kelly; Thomas S. Anderson

doi:10.1117/12.2224299

12 May 2016 A vector relational data modeling approach to Insider threat intelligence

Ryan F. Kelly, Thomas S. Anderson

Proceedings Volume 9831, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII; 98310W (2016) https://doi.org/10.1117/12.2224299
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

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.

Citation Download Citation

Ryan F. Kelly and Thomas S. Anderson "A vector relational data modeling approach to Insider threat intelligence", Proc. SPIE 9831, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VII, 98310W (12 May 2016); https://doi.org/10.1117/12.2224299

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