Publication Date
Volume
21
Issue
4
Start Page
33
File Attachment
V-21_4.pdf5.35 MB
Abstract
Interpreting data from nuclear safeguards and computer security systems is a tedious and time-consuming task. It typically requires the examination of large amounts of data for unusual patterns of activity. Neural networks provide a flexible pattern-recognition capability that can be adapted for these purposes. In this paper we describe a methodology for performing anomaly detection and consistency checking in safeguards and security data.
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