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Proceedings Paper

Behavior-based network management: a unique model-based approach to implementing cyber superiority
Author(s): Jocelyn M. Seng
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Paper Abstract

Behavior-Based Network Management (BBNM) is a technological and strategic approach to mastering the identification and assessment of network behavior, whether human-driven or machine-generated. Recognizing that all five U.S. Air Force (USAF) mission areas rely on the cyber domain to support, enhance and execute their tasks, BBNM is designed to elevate awareness and improve the ability to better understand the degree of reliance placed upon a digital capability and the operational risk.2 Thus, the objective of BBNM is to provide a holistic view of the digital battle space to better assess the effects of security, monitoring, provisioning, utilization management, allocation to support mission sustainment and change control. Leveraging advances in conceptual modeling made possible by a novel advancement in software design and implementation known as Vector Relational Data Modeling (VRDM™), the BBNM approach entails creating a network simulation in which meaning can be inferred and used to manage network behavior according to policy, such as quickly detecting and countering malicious behavior. Initial research configurations have yielded executable BBNM models as combinations of conceptualized behavior within a network management simulation that includes only concepts of threats and definitions of “good” behavior. A proof of concept assessment called “Lab Rat,” was designed to demonstrate the simplicity of network modeling and the ability to perform adaptation. The model was tested on real world threat data and demonstrated adaptive and inferential learning behavior. Preliminary results indicate this is a viable approach towards achieving cyber superiority in today's volatile, uncertain, complex and ambiguous (VUCA) environment.

Paper Details

Date Published: 17 May 2016
PDF: 15 pages
Proc. SPIE 9826, Cyber Sensing 2016, 98260H (17 May 2016); doi: 10.1117/12.2227969
Show Author Affiliations
Jocelyn M. Seng, Air Univ. (United States)

Published in SPIE Proceedings Vol. 9826:
Cyber Sensing 2016
Igor V. Ternovskiy; Peter Chin, Editor(s)

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