Dr. Brian P. Levey Profile

Dr. Brian P. Levey

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Proceedings Article | 8 June 2022 Presentation + Paper

Analytics for early detection of insider threat

Laurie Fenstermacher, Kathleen Larson, Christine Vitiello, Steve Shellman, Brian Levey

Proceedings Volume 12122, 121220O (2022) https://doi.org/10.1117/12.2624111

KEYWORDS: Data mining, Analytics, Web 2.0 technologies, Social sciences, Visualization, Signal detection, Psychology

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Incidents related to insider threats are steadily increasing, especially technology thefts.¹ Dr. Larry Ponemon wrote, “insider threats are not viewed as seriously as external threats, like a cyber-attack. But when companies had an insider threat, in general, they were much more costly than external incidents. This was largely because the insider that is smart has the skills to hide the crime, for months, for years, sometimes forever.”² Insider threat is a relatively rare occurrence, often perpetuated by revenge seeking employees with a grievance against their employer. The capitol assault was an eye-opening event in that it clearly demonstrated that insiders, in this case military and ex-military, were willing to engage in violence against the government. Detection of insider threat is a difficult problem as data is limited and the factors surrounding insider threat are highly contextual. Previous research tends to focus on theoretical perspectives and threat mitigation, frequently emphasizing cyber-technical indicators of insider threat versus focusing on the human behind the screen.³ More behavior focused research has identified several psychosocial, individual-level risk factors for insider threat (e.g., disgruntlement, poor work performance, etc.) or has conducted personality assessments on known insiders post-hoc. ^4,5 However, future directions in this line of research need to address early detection of mobilization as part of the defensive strategy against these threats. Subsequently, this paper will summarize the state-of-the-knowledge in terms of research on behavioral factors and approaches for insider threat detection, highlighting methods for assessing social-cyber information to enable early detection of insider threat.

Proceedings Article | 27 April 2018 Presentation + Paper

Forecasting aggressive state behavior and assessing courses of action to successfully deter hostile adversaries

Brian Levey, Steve Shellman

Proceedings Volume 10653, 106530I (2018) https://doi.org/10.1117/12.2307601

KEYWORDS: Data modeling, Machine learning, Factor analysis, Analytical research, Quantitative analysis, Statistical analysis

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This project employs data extracted from unstructured text and quantitative behavioral models to understand, forecast, and mitigate US adversaries' aggressive actions against the US and our allies. We use a combination of quasi-experimental causal modeling and counterfactual assessment techniques to assess the effectiveness of US courses of action (COAs) to quell aggressive states’ hostile activities. Results illustrate actions may yield unintended consequences through their impacts on other contextual factors. Additional analyses employ forecasting and ensemble techniques to examine the likely anticipated consequences of various US COAs in future scenarios and cases. Ultimately, the data, methods, and results provide a useful decision-support tool for planners and analysts faced with how best to mitigate against unfavorable outcomes.

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