Understanding the structure and dynamics of
networks are of vital importance to winning the global war on
terror. To fully comprehend the network environment, analysts
must be able to investigate interconnected relationships of many
diverse network types simultaneously as they evolve both
spatially and temporally. To remove the burden from the analyst
of making mental correlations of observations and conclusions
from multiple domains, we introduce the Dynamic Graph
Analytic Framework (DYGRAF). DYGRAF provides the
infrastructure which facilitates a layered multi-modal network
analysis (LMMNA) approach that enables analysts to assemble
previously disconnected, yet related, networks in a common
battle space picture. In doing so, DYGRAF provides the analyst
with timely situation awareness, understanding and anticipation
of threats, and support for effective decision-making in diverse
environments.
The Personalized Assistant that Learns (PAL) Program is a Defense Advanced Research Projects Agency (DARPA) research effort that is advancing technologies in the area of cognitive learning by developing
cognitive assistants to support military users, such as commanders and decision makers. The Air Force Research Laboratory's (AFRL) Information Directorate leveraged several core PAL components and
applied them to the Web-Based Temporal Analysis System (WebTAS) so that users of this system can have automated features, such as task learning, intelligent clustering, and entity extraction. WebTAS is a
modular software toolset that supports fusion of large amounts of disparate data sets, visualization, project organization and management, pattern analysis and activity prediction, and includes various presentation aids. WebTAS is predominantly used by analysts within the intelligence community and with the addition
of these automated features, many transition opportunities exist for this integrated technology. Further, AFRL completed an extensive test and evaluation of this integrated software to determine its effectiveness for military applications in terms of timeliness and situation awareness, and these findings and conclusions,
as well as future work, will be presented in this report.
KEYWORDS: Analytical research, Network security, Social network analysis, Detection and tracking algorithms, Social networks, Visualization, Visual analytics, Data processing, Tactical intelligence, Computing systems
Intelligence analysts are tasked with making sense of enormous amounts of data and gaining an awareness of a situation that can be acted upon. This process can be extremely difficult and time consuming. Trying to differentiate between important pieces of information and extraneous data only complicates the problem. When dealing with data containing entities and relationships, social network analysis (SNA) techniques can be employed to make this job easier. Applying network measures to social network graphs can identify the most significant nodes (entities) and edges (relationships) and help the analyst further focus on key areas of concern. Strange developed a model that identifies high value targets such as centers of gravity and critical vulnerabilities. SNA lends itself to the discovery of these high value targets and the Air Force Research Laboratory (AFRL) has investigated several network measures such as centrality, betweenness, and grouping to identify centers of gravity and critical vulnerabilities. Using these network measures, a process for the intelligence analyst has been developed to aid analysts in identifying points of tactical emphasis. Organizational Risk Analyzer (ORA) and Terrorist Modus Operandi Discovery System (TMODS) are the two applications used to compute the network measures and identify the points to be acted upon. Therefore, the result of leveraging social network analysis techniques and applications will provide the analyst and the intelligence community with more focused and concentrated analysis results allowing them to more easily exploit key attributes of a network, thus saving time, money, and manpower.
Conference Committee Involvement (6)
Machine Intelligence and Bio-inspired Computation: Theory and Applications VII
2 May 2013 | Baltimore, Maryland, United States
Evolutionary and Bio-inspired Computation: Theory and Applications VI
25 April 2012 | Baltimore, Maryland, United States
Evolutionary and Bio-Inspired Computation: Theory and Applications V
27 April 2011 | Orlando, Florida, United States
Evolutionary and Bio-Inspired Computation: Theory and Applications IV
7 April 2010 | Orlando, Florida, United States
Evolutionary and Bio-Inspired Computation: Theory and Applications III
14 April 2009 | Orlando, Florida, United States
Evolutionary and Bio-Inspired Computation: Theory and Applications II
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