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Detecting anomalous nuclear materials accounting transactions: Applying machine learning to plutonium processing facilities

Nuclear materials accountancy is the only safeguards measure that provides direct evidence of the status of nuclear materials. Of the six categories that gives rise to inventory differences, the technical capability is now in place to implement the technical innovations necessary to reduce the human error categories. There are really three main approaches to detecting anomalies in materials control and accountability (MC A) data: (1) Statistical: numeric methods such as the Page's Test, CUSUM, CUMUF, SITMUF, etc., can detect anomalies in metric (numeric) data. (2) Expert systems: Human expert's rules can be encoded into software systems such as ART, KEE, or Prolog. (3) Machine learning: Training data, such as historical MC A records, can be fed to a classifier program or neutral net or other machine learning algorithm. The Wisdom Sense (W S) software is a combination of approaches 2 and 3. The W S program includes full features for adding administrative rules and expert judgment rules to the rule base. if desired, the software can enforce consistency among all rules in the rule base.