Publications

Advances on differentiating between malicious intent and natural "organizational evolution"to explain observed anomalies in operational workplace patterns suggest benefit from evaluating collective behaviors observed in the facilities to improve insider threat detection and mitigation (ITDM). Advances in artificial neural networks (ANN) provide more robust pathways for capturing, analyzing, and collating disparate data signals into quantitative descriptions of operational workplace patterns. In response, a joint study by Sandia National Laboratories and the University of Texas at Austin explored the effectiveness of commercial artificial neural network (ANN) software to improve ITDM. This research demonstrates the benefit of learning patterns of organizational behaviors, detecting off-normal (or anomalous) deviations from these patterns, and alerting when certain types, frequencies, or quantities of deviations emerge for improving ITDM. Evaluating nearly 33,000 access control data points and over 1,600 intrusion sensor data points collected over a nearly twelve-month period, this study's results demonstrated the ANN could recognize operational patterns at the Nuclear Engineering Teaching Laboratory (NETL) and detect off-normal behaviors - suggesting that ANNs can be used to support a data-analytic approach to ITDM. Several representative experiments were conducted to further evaluate these conclusions, with the resultant insights supporting collective behavior-based analytical approaches to quantitatively describe insider threat detection and mitigation.

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China is endeavoring to build nuclear power plants (NPPs) in numerous countries around the globe - an initiative that has the potential to strengthen Chinas political and economic influences on those countries. This study provides an overview of the situation and considers the issues involved in such partnerships with China. In order to assess Chinas ability to follow through with its agreements, this study also presents a technical review of its NPP production capability.

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In recent years, insider threat programs have become an important aspect of nuclear security, and nuclear security training courses. However, many nuclear security insider threat programs fail to address the insider threat attack and monitoring potential that exists on information technology (IT) systems. This failure is critical because of the importance of information technology and networks in today’s world. IT systems offer an opportunity to perpetrate dangerous insider attacks, but they also present an opportunity to monitor for them and prevent them. This paper suggests a number of best practices for monitoring and preventing insider attacks on IT systems, and proposes the development of a new IT insider threat tabletop that can be used to help train nuclear security practitioners on how best to implement IT insider threat prevention best practices. The development of IT insider threat best practices and a practical tabletop exercise will allow nuclear security practitioners to improve nuclear security trainings as it integrates a critical part of insider threat prevention into the broader nuclear security system.