XLab: Early Indications & Warnings from Open Source Data with Application to Biological Threat

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dc.contributor.authorSimek, Olga
dc.contributor.authorDavis, Curtis
dc.contributor.authorHeier, Andrew
dc.contributor.authorMohindra, Sanjeev
dc.contributor.authorO'Brien, Kyle
dc.contributor.authorPassarelli, John
dc.contributor.authorWaugh, Frederick
dc.date.accessioned2017-12-28T00:42:44Z
dc.date.available2017-12-28T00:42:44Z
dc.date.issued2018-01-03
dc.description.abstractXLab is an early warning system that addresses a broad range of national security threats using a flexible, rapidly reconfigurable architecture. XLab enables intelligence analysts to visualize, explore, and query a knowledge base constructed from multiple data sources, guided by subject matter expertise codified in threat model graphs. This paper describes a novel system prototype that addresses threats arising from biological weapons of mass destruction. The prototype applies knowledge extraction analytics-”including link estimation, entity disambiguation, and event detection-”to build a knowledge base of 40 million entities and 140 million relationships from open sources. Exact and inexact subgraph matching analytics enable analysts to search the knowledge base for instances of modeled threats. The paper introduces new methods for inexact matching that accommodate threat models with temporal and geospatial patterns. System performance is demonstrated using several simplified threat models and an embedded scenario.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2018.118
dc.identifier.isbn978-0-9981331-1-9
dc.identifier.urihttp://hdl.handle.net/10125/50005
dc.language.isoeng
dc.relation.ispartofProceedings of the 51st Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDecision Support for Complex Networks
dc.subjectindications and warnings, graph matching, threat models
dc.titleXLab: Early Indications & Warnings from Open Source Data with Application to Biological Threat
dc.typeConference Paper
dc.type.dcmiText

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