We will introduce a new framework called cyber zone defense (CZD) that treats malware like a black box: a process we can study solely based on its internal and external communication. We can reduce the impact of malware, without regard to its functionality or even existence, by limiting only these connections. In this paper, we propose two metrics for measuring CZD effectiveness and an illustrative simulation and a closed form mathematical model that predicts these statistics. The simulation is intuitive; it allows the analyst to provision arbitrary configurations and see how changes in topology affect the efficacy of the CZD. The model provides a mathematical verification for CZD and matches the results of the simulation well. These artifacts test the feasibility of CZD while deferring implementation details.
The thermal conductivity of n- and p-type doped gallium nitride (GaN) epilayers having thicknesses of 3-4 μm was investigated using time domain thermoreflectance. Despite possessing carrier concentrations ranging across 3 decades (1015-1018cm-3), n-type layers exhibit a nearly constant thermal conductivity of 180 W/mK. The thermal conductivity of p-type epilayers, in contrast, reduces from 160 to 110 W/mK with increased doping. These trends - and their overall reduction relative to bulk - are explained leveraging established scattering models where it is shown that, while the decrease in p-type layers is partly due to the increased impurity levels evolving from its doping, size effects play a primary role in limiting the thermal conductivity of GaN layers tens of microns thick. Device layers, even of pristine quality, will therefore exhibit thermal conductivities less than the bulk value of 240 W/mK owing to their finite thickness.
The war to establish cyber supremacy continues, and the literature is crowded with strictly technical cyber security measures. We present the results of a three year LDRD project using Linkography, a methodology new to the field of cyber security, we establish the foundation necessary to track and profile the microbehavior of humans attacking cyber systems. We also propose ways to leverage this understanding to influence and deceive these attackers. We studied the science of linkography, applied it to the cyber security domain, implemented a software package to manage linkographs, generated the preprocessing blocks necessary to ingest raw data, produced machine learning models, created ontology refinement algorithms and prototyped a web application for researchers and practitioners to apply linkography. Machine learning produced some of our key results: We trained and validated multinomial classifiers with a real world data set and predicted the attacker's next category of action with 86 to 98% accuracy; dimension reduction techniques indicated that the linkography-based features were among the most powerful. We also discovered ontology refinement algorithms that advanced the state of the art in linkography in general and cyber security in particular. We conclude that linkography is a viable tool for cyber security; we look forward to expanding our work to other data sources and using our prediction results to enable adversary deception techniques.
Remote sensing systems have firmly established a role in providing immense value to commercial industry, scientific exploration, and national security. Continued maturation of sensing technology has reduced the cost of deploying highly-capable sensors while at the same time increased reliance on the information these sensors can provide. The demand for time on these sensors is unlikely to diminish. Coordination of next-generation sensor systems, larger constellations of satellites, unmanned aerial vehicles, ground telescopes, etc. is prohibitively complex for existing heuristics-based scheduling techniques. The project was a two-year collaboration spanning multiple Sandia centers and included a partnership with Texas A&M University. We have developed algorithms and software for collection scheduling, remote sensor field-of-view pointing models, and bandwidth-constrained prioritization of sensor data. Our approach followed best practices from the operations research and computational geometry communities. These models provide several advantages over state of the art techniques. In particular, our approach is more flexible compared to heuristics that tightly couple models and solution techniques. First, our mixed-integer linear models afford a rigorous analysis so that sensor planners can quantitatively describe a schedule relative to the best possible. Optimal or near-optimal schedules can be produced with commercial solvers in operational run-times. These models can be modified and extended to incorporate different scheduling and resource constraints and objective function definitions. Further, we have extended these models to proactively schedule sensors under weather and ad hoc collection uncertainty. This approach stands in contrast to existing deterministic schedulers which assume a single future weather or ad hoc collection scenario. The field-of-view pointing algorithm produces a mosaic with the fewest number of images required to fully cover a region of interest. The bandwidth-constrained algorithms find the highest priority information that can be transmitted. All of these are based on mixed-integer linear programs so that, in the future, collection scheduling, field-of-view, and bandwidth prioritization can be combined into a single problem. Experiments conducted using the developed models, commercial solvers, and benchmark datasets have demonstrated that proactively scheduling against uncertainty regularly and significantly outperforms deterministic schedulers.
Sandia National Laboratories’ California site is celebrating its 60th anniversary (1956 to 2016), and high performance computing has been a key enabler for its scientists and engineers throughout much of its history. Since its founding, Sandia California has helped pioneer the use of HPC platforms including hosting Sandia’s first Cray-1 supercomputer in the 1970s and supporting development of scalable cluster computing platforms to create a new paradigm for cost-effective supercomputing in the 1990s. Recent decades of investment in creation of scalable application frameworks for scientific computing have also enabled new generations of modeling and simulation codes. These resources have facilitated computational analysis of complex phenomena in diverse applications spanning national defense, energy, and homeland security. Today, Sandia California researchers work with partners in academia, industry, and national labs to evolve the state-of-the-art in HPC, modeling, and data analysis (including foundational capabilities for exascale computing platforms) and apply them in transformational ways. Research efforts include mitigating the effects of silent hardware failures that can jeopardize the results of large-scale computations, developing exascale-capable asynchronous task-parallel programming models and runtime systems, formulating new techniques to better explore and analyze extreme-scale data sets, and increasing our understanding of materials and chemical sciences which has applications spanning nuclear weapons stockpile stewardship to more efficient automobile engines. The following section highlights some of these research and applications projects and further illustrates the breadth of our HPC capabilities.
Optical diagnostics play a central role in dynamic compression research. Currently, streak cameras are employed to record temporal and spectroscopic information in single-event experiments, yet are limited in several ways; the tradeoff between time resolution and total record duration is one such limitation. This project solves the limitations that streak cameras impose on dynamic compression experiments while reducing both cost and risk (equipment and labor) by utilizing standard high-speed digitizers and commercial telecommunications equipment. The missing link is the capability to convert the set of experimental (visible/x-ray) wavelengths to the infrared wavelengths used in telecommunications. In this report, we describe the problem we are solving, our approach, our results, and describe the system that was delivered to the customer. The system consists of an 8-channel visible-to-infrared converter with > 2 GHz 3-dB bandwidth.
The purpose of this Failure Modes and Effects Analysis (FMEA) is chosen to examine the potential failures of the systems which could result in an overspeed event with a potential for flying debris from the wind turbine blades. The FMEA method was chosen to examine the turbine hydraulic system because two important turbine protective features use hydraulic pressure to function: the blade pitching system, and the brake. The objective of the FMEA was to determine if the two safety features have a likely common failure point, or if the safety systems can be individually credited for protection of the turbine.
Particle methods in computational physics are useful for modeling the motion of fluids and solids subject to large deformations. Under these conditions, mesh-based approaches often fail due to decreasing element quality leading to inaccuracy and instability. The developed software package called Moab investigates and prototypes next-generation particle methods, focusing on rigorous error analysis and active error minimization strategies during the computation. The present work discusses examples calculations representative of real engineering problems with quantified and maximized accuracy while demonstrating the potential for meeting engineering performance requirements.
