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Results from Invoking Artificial Neural Networks to Measure Insider Threat Detection & Mitigation

Digital Threats: Research and Practice

Williams, Adam D.; Bays, Nathan R.; Shoman, Nathan; Charlton, William S.

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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TYPE Journal Article YEAR 2022
DOIOSTIScopus

Sandia National Laboratories Early Career University Faculty Mentoring Program in International Safeguards

Solodov, Alexander; Peter-Stein, Natacha; Hartig, Kyle C.; Padilla, Eduardo A.; Di Fulvio, Angela; Shoman, Nathan

Recent years have seen a significantly increased focus in the areas of knowledge retention and mentoring of junior staff within the U.S. national laboratory complex. In order to involve the university community in this process, as well, an international safeguards mentoring program was established by Sandia National Laboratories (SNL) for early career university faculty. After a successful experience during 2019, the program continued into 2020 to include two new faculty members who were paired with SNL subject matter experts based on the topic of their individual projects: one to work on advanced laboratory work for physics, technology, and policy of nuclear safeguards and nonproliferation, and the other to look at machine learning applied to international safeguards and nonproliferation. There is a two-pronged purpose to the program: fostering the development of educational resources available for international safeguards and exploring new research topics stemming from the exchange of mentor and mentee. Further, the program as a whole allows for junior faculty members to establish and expand a relationship network within international safeguards. In addition, programs such as this build stronger connections between the academic and the national laboratory community. Thanks to the junior faculty members that now have new connections into the laboratory community and potential for collaboration projects with the laboratories in the future, safeguards knowledge can actually increase far beyond just individually engaging students using this new and efficient avenue.

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TYPE SAND Report YEAR 2020
DOIDOIOSTIOSTI
Results 26–50 of 66
Results 26–50 of 66
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