Developing an intelligence analysis process through social network analysis

Todd Waskiewicz; Peter LaMonica

doi:10.1117/12.776990

1 May 2008 Developing an intelligence analysis process through social network analysis

Todd Waskiewicz, Peter LaMonica

Proceedings Volume 6964, Evolutionary and Bio-Inspired Computation: Theory and Applications II; 69640B (2008) https://doi.org/10.1117/12.776990
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

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.

Citation Download Citation

Todd Waskiewicz and Peter LaMonica "Developing an intelligence analysis process through social network analysis", Proc. SPIE 6964, Evolutionary and Bio-Inspired Computation: Theory and Applications II, 69640B (1 May 2008); https://doi.org/10.1117/12.776990

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