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Title: Automated Detection of Anomalous Shipping Manifests to Identify Illicit Trade

We describe an approach to analyzing trade data which uses clustering to detect similarities across shipping manifest records, classification to evaluate clustering results and categorize new unseen shipping data records, and visual analytics to provide to support situation awareness in dynamic decision making to monitor and warn against the movement of radiological threat materials through search, analysis and forecasting capabilities. The evaluation of clustering results through classification and systematic inspection of the clusters show the clusters have strong semantic cohesion and offer novel ways to detect transactions related to nuclear smuggling.
Authors:
;
Publication Date:
OSTI Identifier:
1117085
Report Number(s):
PNNL-SA-94488
DOE Contract Number:
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: IEEE Conference on Technologies for Homeland Security (HST 2013), November 12-14, 2013, Waltham, MA, 529-534
Publisher:
Institute of Electrical and Electronics Engineers, Piscataway, NJ, United States(US).
Research Org:
Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
trade data; clustering; classification; visual analytics; illicit trafficking; detection of radiological threat materials; nuclear smuggling