Graph-Based Analysis of Nuclear Smuggling Data

被引：1
作者：
Cook, Diane [1 ]
Holder, Lawrence [1 ]
Thompson, Sandy [2 ]
Whitney, Paul [2 ]
Chilton, Lawrence [2 ]
机构：
[1] Washington State Univ, Pullman, WA 99164 USA
[2] Pacific Northwest Natl Lab, Richland, WA USA
来源：
JOURNAL OF APPLIED SECURITY RESEARCH | 2009年 / 4卷 / 04期
关键词：
Nuclear smuggling; data mining; graph representation; pattern discovery;
D O I：
10.1080/19361610903176310
中图分类号：
DF [法律]; D9 [法律];
学科分类号：
0301 ;
摘要：
Much of the data that is collected and analyzed today is structural, consisting not only of entities but also of relationships between the entities. As a result, analysis applications rely on automated structural data mining approaches to find patterns and concepts of interest. This ability to analyze structural data has become a particular challenge in many security-related domains. In these domains, focusing on the relationships between entities in the data is critical to detect important underlying patterns. In this study we apply structural data mining techniques to automate analysis of nuclear smuggling data. In particular, we choose to model the data as a graph and use graph-based relational learning to identify patterns and concepts of interest in the data. In this article, we identify the analysis questions that are of importance to security analysts and describe the knowledge representation and data mining approach that we adopt for this challenge. We analyze the results using the Russian nuclear smuggling event database.
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收藏
页码：501 / 517
页数：17
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