Publications

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Detecting Communities and Attributing Purpose to Human Mobility Data

Proceedings - Winter Simulation Conference

John, Esther W.; Cauthen, Katherine R.; Brown, Nathanael J.; Nozick, Linda K.

Many individuals' mobility can be characterized by strong patterns of regular movements and is influenced by social relationships. Social networks are also often organized into overlapping communities which are associated in time or space. We develop a model that can generate the structure of a social network and attribute purpose to individuals' movements, based solely on records of individuals' locations over time. This model distinguishes the attributed purpose of check-ins based on temporal and spatial patterns in check-in data. Because a location-based social network dataset with authoritative ground-truth to test our entire model does not exist, we generate large scale datasets containing social networks and individual check-in data to test our model. We find that our model reliably assigns community purpose to social check-in data, and is robust over a variety of different situations.

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TYPE Conference Paper YEAR 2021
ScopusOSTIDOI

A Minimally Supervised Event Detection Method

Lecture Notes in Networks and Systems

Hoffman, Matthew J.; Bussell, Sam; Brown, Nathanael J.

Solving classification problems with machine learning often entails laborious manual labeling of test data, requiring valuable time from a subject matter expert (SME). This process can be even more challenging when each sample is multidimensional. In the case of an anomaly detection system, a standard two-class problem, the dataset is likely imbalanced with few anomalous observations and many “normal” observations (e.g., credit card fraud detection). We propose a unique methodology that quickly identifies individual samples for SME tagging while automatically classifying commonly occurring samples as normal. In order to facilitate such a process, the relationships among the dimensions (or features) must be easily understood by both the SME and system architects such that tuning of the system can be readily achieved. The resulting system demonstrates how combining human knowledge with machine learning can create an interpretable classification system with robust performance.

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TYPE Conference Poster YEAR 2021
ScopusOSTI

A Complex Systems Approach to More Resilient Multi-Layered Security Systems

Jones, Katherine A.; Bandlow, Alisa B.; Waddell, Lucas W.; Nozick, Linda K.; Levin, Drew L.; Brown, Nathanael J.

In July 2012, protestors cut through security fences and gained access to the Y-12 National Security Complex. This was believed to be a highly reliable, multi-layered security system. This report documents the results of a Laboratory Directed Research and Development (LDRD) project that created a consistent, robust mathematical framework using complex systems analysis algorithms and techniques to better understand the emergent behavior, vulnerabilities and resiliency of multi-layered security systems subject to budget constraints and competing security priorities. Because there are several dimensions to security system performance and a range of attacks that might occur, the framework is multi-objective for a performance frontier to be estimated. This research explicitly uses probability of intruder interruption given detection (PI) as the primary resilience metric. We demonstrate the utility of this framework with both notional as well as real-world examples of Physical Protection Systems (PPSs) and validate using a well-established force-on-force simulation tool, Umbra.

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TYPE SAND Report YEAR 2016
OSTIDOI

Multi-layered security investment optimization using a simulation embedded within a genetic algorithm

Proceedings - Winter Simulation Conference

Brown, Nathanael J.; Jones, Katherine A.; Nozick, Linda K.; Xu, Ningxiong

The performance of a multi-layered security system, such as those protecting high-value facilities or critical infrastructures, is characterized using several different attributes including detection and interruption probabilities, costs, and false/nuisance alarm rates. The multitude of technology options, alternative locations and configurations for those technologies, threats to the system, and resource considerations that must be weighed make exhaustive evaluation of all possible architectures extremely difficult. This paper presents an optimization model and a computationally efficient solution procedure to identify an estimated frontier of system configuration options which represent the best design choices for the user when there is uncertainty in the response time of the security force, once an intrusion has been detected. A representative example is described.

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TYPE Conference Poster YEAR 2016
ScopusOSTI

Optimizing the Configuration of Sensor Networks to Detect Intruders

Sandia journal manuscript; Not yet accepted for publication

Brown, Nathanael J.; Jones, Katherine A.; Nozick, Linda K.; Xu, Ningxiong X.

This paper focuses on optimizing the selection and configuration of detection technologies to protect a target of interest. The ability of an intruder to simply reach the target is assumed to be sufficient to consider the security system a failure. To address this problem, we develop a game theoretic model of the strategic interactions between the system owner and a knowledgeable intruder. A decomposition-based exact method is used to solve the resultant model.

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TYPE Journal Article YEAR 2015
OSTI

Generalized blockmodeling of multiple valued networks

Social Networks

Jones, Dean A.; Brown, Nathanael J.

This paper presents an extension to generalized blockmodeling where there are more than two types of objects to be clustered based on valued network data. We use the ideas in homogeneity block modeling to develop an optimization model to perform the clustering of the objects and the resulting partitioning of the ties so as to minimize the inconsistency of an empirical block with an ideal block. The ideal block types used in this modeling are null, complete and a new type that is related to that developed in Ziberna (2007). Three case studies are presented, two based on the Southern Women dataset (Davis et al. 1941) and a third based on passenger air travel in the Continental United States.

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TYPE Journal Article YEAR 2011
OSTIDOI

Optimal recovery sequencing for critical infrastructure resilience assessment

Vugrin, Eric D.; Brown, Nathanael J.

Critical infrastructure resilience has become a national priority for the U. S. Department of Homeland Security. System resilience has been studied for several decades in many different disciplines, but no standards or unifying methods exist for critical infrastructure resilience analysis. This report documents the results of a late-start Laboratory Directed Research and Development (LDRD) project that investigated the identification of optimal recovery strategies that maximize resilience. To this goal, we formulate a bi-level optimization problem for infrastructure network models. In the 'inner' problem, we solve for network flows, and we use the 'outer' problem to identify the optimal recovery modes and sequences. We draw from the literature of multi-mode project scheduling problems to create an effective solution strategy for the resilience optimization model. We demonstrate the application of this approach to a set of network models, including a national railroad model and a supply chain for Army munitions production.

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TYPE SAND Report YEAR 2010
OSTIDOI
48 Results
48 Results