Operationalizing Insider Threat Potential and Risk-Significant Insiders to Enhance Insider Threat Detection and Mitigation
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Williams, Adam David↗
Engineering topics
Abbott, Shannon
Publications and source records attributed to Abbott, Shannon.
DERIVING A FRAMEWORK FOR INSIDER RISK POTENTIAL USING ARTIFICIAL NEURAL NETWORKS FOR INSIDER THREAT DETECTION & MITIGATION.
Abstract not provided.
Heffington, Colton↗
DERIVING A FRAMEWORK FOR ?INSIDER RISK POTENTIAL? FROM USING ARTIFICIAL NEURAL NETWORKS FOR INSIDER THREAT DETECTION & MITIGATION.
Abstract not provided.
Heffington, Colton↗
Summary Results From Invoking Artificial Neural Networks To Measure Insider Threat Mitigation.
Abstract not provided.
Abbott, Shannon↗
China Civilian Nuclear Power Reactor Study
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.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Phase I Closeout Report: Invoking Artificial Neural Networks to Measure Insider Threat Mitigation
Researchers from Sandia National Laboratories (Sandia) and the University of Texas at Austin (UT) conducted this study to explore the effectiveness of commercial artificial neural network (ANN) software to improve insider threat detection and mitigation (ITDM). This study hypothesized that ANNs could be "trainee to learn patterns of organizational behaviors, detect off-normal (or anomalous) deviations from these patterns, and alert when certain types, frequencies, or quantities of deviations emerge. The ReconaSense ANN system was installed at UT's Nuclear Engineering Teaching Laboratory (NETL) and collected 13,653 access control data points and 694 intrusion sensor data points over a three-month period. Preliminary analysis of this baseline data demonstrated regularized patterns of life in the facility, and that off-normal behaviors are detectable under certain situations -- even for a facility with anticipated highly non-routine, operational behaviors. Completion of this pilot study demonstrated how the ReconaSense ANN could be used to identify expected operational patterns and detect unexpected anomalous behaviors in support of a data-analytic approach to ITDM. While additional studies are needed to fully understand and characterize this system, the results of this initial study are overall very promising for demonstrating a new framework for ITDM utilizing ANNs and data analysis techniques.
97 MATHEMATICS AND COMPUTING↗
Preliminary Results From Invoking Artificial Neural Networks To Measure Insider Threat Mitigation.
Abstract not provided.
Abbott, Shannon↗
Preliminary Results From Invoking Artificial Neural Networks To Measure Insider Threat Mitigation.
Abstract not provided.
Abbott, Shannon↗
Malicious Threat Anticipation using an Adaptive Complex Systems Approach.
Abstract not provided.
Caskey, Susan A.↗