Publications

Abstract not provided.

Current protection strategies against insider adversaries are expensive, intrusive, not systematically implemented, and operate independently; too often, these strategies are defeated. The authors discuss the development of methods for a systems-based approach to insider security. To investigate insider evolution within an organization, they use system dynamics to develop a preliminary model of the employee life cycle that defines and analyzes the employee population's interactions with insider security protection strategies. The authors exercised the model for an example scenario that focused on human resources and personnel security activitiesspecifically, prehiring screening and security clearance processes. The model provides a framework for understanding important interactions, interdependencies, and gaps in insider protection strategies. This work provides the basis for developing an integrated systems-based process for buildingthat is, designing, evaluating, and operatinga system for effective insider security. © 2009 IEEE.

Abstract not provided.

For applications such as force protection, an effective decision maker needs to maintain an unambiguous grasp of the environment. Opportunities exist to leverage computational mechanisms for the adaptive fusion of diverse information sources. The current research employs neural networks and Markov chains to process information from sources including sensors, weather data, and law enforcement. Furthermore, the system operator's input is used as a point of reference for the machine learning algorithms. More detailed features of the approach are provided, along with an example force protection scenario.

Natural language processing-based knowledge management software, traditionally developed for security organizations, is now becoming commercially available. An informal survey was conducted to discover and examine current NLP and related technologies and potential applications for information retrieval, information extraction, summarization, categorization, terminology management, link analysis, and visualization for possible implementation at Sandia National Laboratories. This report documents our current understanding of the technologies, lists software vendors and their products, and identifies potential applications of these technologies.

Abstract not provided.

This report describes the results of a Laboratory-Directed Research and Development project on techniques for pattern discovery in discrete event time series data. In this project, we explored two different aspects of the pattern matching/discovery problem. The first aspect studied was the use of Dynamic Time Warping for pattern matching in continuous data. In essence, DTW is a technique for aligning time series along the time axis to optimize the similarity measure. The second aspect studied was techniques for discovering patterns in discrete event data. We developed a pattern discovery tool based on adaptations of the A-priori and GSP (Generalized Sequential Pattern mining) algorithms. We then used the tool on three different application areas--unattended monitoring system data from a storage magazine, computer network intrusion detection, and analysis of robot training data.