Leticia Cuellar-Hengartner

The Probabilistic Effectiveness Methodology (PEM) is a simulation tool with a holistic approach to risk assessment of nuclear smuggling. PEM simulates valid representations of threat motivation, capabilities, and intent, threat transportation pathways (air, land, and sea), the performance of detector architectures, and individual detector performance associated with preventive radiological and nuclear detection. Further, it analyses from a Red/Adversary perspective, gaps, seams and vulnerabilities of the Global Nuclear Detection Architecture (GNDA). This paper presents the different PEM components and illustrates (through use of notional data) several examples of how PEM can support the decision making process for GNDA problems.

The shortest path problem on a network with fixed weights is a well studied problem with applications to many diverse areas such as transportation and telecommunications. We are particularly interested in the scenario where a nuclear material smuggler tries to succesfully reach herlhis target by identifying the most likely path to the target. The identification of the path relies on reliabilities (weights) associated with each link and node in a multi-modal transportation network. In order to account for the adversary's uncertainty and to perform sensitivity analysis we introduce random reliabilities. We perform some controlled experiments on the grid and present the distributional properties of the resulting stochastic shortest paths.

Nuclear weapons proliferation is an existing and growing worldwide problem. To help with devising strategies and supporting decisions to interdict the transport of nuclear material, we developed the Pathway Analysis, Threat Response and Interdiction Options Tool (PATRIOT) that provides an analytical approach for evaluating the probability that an adversary smuggling radioactive or special nuclear material will be detected during transit. We incorporate a global, multi-modal transportation network, explicit representation of designed and serendipitous detection opportunities, and multiple threat devices, material types, and shielding levels. This paper presents the general structure of PATRIOT, and focuses on the theoretical framework used to model the reliabilities of all network components that are used to predict the most likely pathways to the target.

Soft cosmic ray tomography has been shown to successfully discriminate materials with various density levels due to their ability to deeply penetrate matter, allowing sensitivity to atomic number, radiation length and density. Because the multiple muon scattering signal from high Z-materials is very strong, the technology is well suited to the detection of the illicit transportation of special and radiololgical nuclear materials. In addition, a recent detection technique based on measuring the lower energy particles that do not traverse the material (range radiography), allows to discriminate low and medium Z-materials. We have demonstrated it first using Monte Carlo simulations. More recently, using a Mini-Muon Tracker developed at Los Alamos National Laboratory, we performed various experiments to try out the radiation length technology. This paper presents the results from real experiments and evaluates the likelihood that soft cosmic ray tomography may be applied to detect high-explosives.

This paper presents a a general framework for applying individual decision models to aggregated populations. Our approach is useful for modeling and predicting evacuation decisions from disasters, ranging from earthquakes, flooding and wild fires, to industrial emergencies like chemical spills or nuclear accidents, to reactions to terrorism attacks. The novelty of our approach is to apply well-documented household evacuation behavioral models to statistical accurate synthetic populations with detailed demographic information. Predictions of who evacuates and who does not evacuate, and the type and location of the selected shelters is useful for emergency management and planning. We illustrate it by applying our tools to predict emergency relocation behavior from hurricanes.

The rising popularity of mobile devices, such as cellular phones and PDAs, has made them a lucrative playground for mobile malware propagation. One common infection vector exploited by these mobile malware is Bluetooth. In this paper, we propose an architecture called Blue-Watchdog that detects Bluetooth worm propagation in public areas based on statistical methods. To achieve fast and accurate Bluetooth worm detection, Blue-Watchdog monitors abrupt changes of average paging rate per Bluetooth device from both temporal and temporal-spatial perspectives. The temporal scheme relies on the CUSUM (Cumulative Sum sequential test together with the generalized likelihood ratio (GLR), and the temporal-spatial scheme aims to identify spatial regions with abnormally frequent paging attempts. Experimental results show that Blue-Watchdog not only has low false alarm rates, but also effectively detects Bluetooth worms that spread quickly in areas where Bluetooth devices are greatly mixed due to high nobility and also those that, propagate relatively slowly in a spatially constrained fashion.

Charged particle tomography has been gaining ground due to its unique probing characteristics, and the ability of cosmic ray muons to deeply penetrate matter, allowing sensitivity to atomic number, radiation length and material density via detection of the probing particles before and after target traversal. But not all particles completely traverse the target volume. This paper explores the usefulness and information content in the stopped particles, referred to as soft particles. We show, in simulations, that the stopped particles are sensitive to low Z material and therefore provides complementary information to the scattering data.

The alarm that worms start to spread on increasingly popular mobile devices calls for an in-depth investigation of their propagation dynamics. In this paper, we study how mobility patterns affect Bluetooth worm spreading speeds. We find that the impact of mobility patterns is substantial over a large set of of changing Bluetooth and worm parameters. For instance, a mobility model under which devices move among a fixed set of activity locations can result in worm propagation speeds four times faster than a classical mobility model such as the random walk model. Our investigation reveals that the key factors affecting Bluetooth worm propagation speeds include spatial distributions of nodes, link duration distributions, degrees to which devices are mixed together, and even the burstiness of successive links.

We propose a rigorous mathematical interpretation of a novel family of Active Queue Management schemes, called Sending Rate Estimate based Queue Management (SREQM) scheme, that aims to provide fair bandwidth allocation to all the flows in a router by estimating the flow sending rates, while maintaining only minimal per-flow state information. We propose an optimized implementation of SREQM, called Fair Sending Rate Estimate based Queue Management (FSREQM) scheme, and show through comparative simulation that FRESQM is the only scheme among those tested that successfully prevents bandwidth abuse while maintaining high link utilization.