Using Side Channel Information and Artificial Intelligence for Malware Detection

Cybersecurity continues to be a difficult issue for society especially as the number of networked systems grows. Techniques to protect these systems range from rules-based to artificial intelligence-based intrusion detection systems and anti-virus tools. These systems rely upon the information contained in the network packets and download executables to function. Side channel information leaked from hardware has been shown to reveal secret information in systems such as encryption keys. This work demonstrates that side channel information can be used to detect malware running on a computing platform without access to the code involved.Comment: 7 page

Integrating Spatial Implications into Solving Life-Cycle Challenges of Biofuels and Industrial Symbiosis

Biofuels have demonstrated great promise for global energy production. In the United States, the Renewable Fuel Standard (RFS2) of the Energy Independence and Security Act of 2007 (EISA) calls for the production of 15.2 billion gallons of renewable fuels per year by 2013, 1.28 billion gallons of which need to be biomass-based diesel. Increased biofuel production can help meet rising energy demands, however most biofuel production processes are land- and nutrient-intensive, and must be managed throughout the life cycle to ensure sustainability. The goal of this dissertation was to evaluate industrial symbiosis as a sustainable approach to U.S. biofuel and energy production by creating a framework using GIS that integrates the spatial implications of land and nutrient supply. Defined by the synergistic collaboration of industries enabled by geographic proximity, industrial symbiosis is a key element in resource conservation, as it uses traditionally defined waste outputs as resource inputs. Four systems were examined in this dissertation: a coupled wastewater-power plant system, a sunflower biodiesel production system using urban marginal land, a national biodiesel production system using contaminated waste sites, and an algal biodiesel production system using wastewater and waste CO2. Results from the wastewater-power plant system indicated that secondary-treated wastewater can provide cooling water to power plants, however traditional metrics and tools used to evaluate sustainability are inadequate for such complexity. Spatial assessment is needed to efficiently design transportation and conveyance within the system. Two land-identification frameworks were created using GIS to identify regional and national “waste” lands, or marginal lands, and to evaluate these lands for sunflower, soybean, and algal biodiesel production. A nutrient-availability framework was also created to identify synergistic opportunities for algal biodiesel production. While regional production of sunflower biodiesel generated trivial contributions to the RFS2, marginal sites at the national level could meet 7 to 19% of the RFS2, depending on the distribution of feedstocks. Algal biodiesel produced using waste CO2 and wastewater could meet 0.3% to 17% of the RFS2, depending on the nutrient concentration of the wastewater. These ranges highlight spatial variability of results and emphasize the benefit of GIS in life-cycle sustainability studies

The Effect of a Drinking Water Education Program on the Threat Perception of Nitrate Contaminated Water by Pregnant Women

The purpose of this study is to determine the effects of a drinking water education program in a rural community on the perceived threat of nitrate-contaminated water by pregnant women. The proposed study aims to reach out to at least 120 pregnant women in their first trimester. Data will be collected using a Protection Motivation Theory (PMT) scale pretest and posttest that will gauge pregnant women’s perceived threat of nitrate-contaminated drinking water before and after the education program. The data will be analyzed using descriptive and inferential statistics. A paired sample t-test will be used to determine the effects of the drinking water education program. Limitations to the study include accessibility to the target population, the self-reporting questionnaire, and limited time spent with the participants

Defining freshwater as a natural resource: a framework linking water use to the area of protection natural resources

© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Purpose: While many examples have shown unsustainable use of freshwater resources, existing LCIA methods for water use do not comprehensively address impacts to natural resources for future generations. This framework aims to (1) define freshwater resource as an item to protect within the Area of Protection (AoP) natural resources, (2) identify relevant impact pathways affecting freshwater resources, and (3) outline methodological choices for impact characterization model development. Methods: Considering the current scope of the AoP natural resources, the complex nature of freshwater resources and its important dimensions to safeguard safe future supply, a definition of freshwater resource is proposed, including water quality aspects. In order to clearly define what is to be protected, the freshwater resource is put in perspective through the lens of the three main safeguard subjects defined by Dewulf et al. (2015). In addition, an extensive literature review identifies a wide range of possible impact pathways to freshwater resources, establishing the link between different inventory elementary flows (water consumption, emissions, and land use) and their potential to cause long-term freshwater depletion or degradation. Results and discussion: Freshwater as a resource has a particular status in LCA resource assessment. First, it exists in the form of three types of resources: flow, fund, or stock. Then, in addition to being a resource for human economic activities (e.g., hydropower), it is above all a non-substitutable support for life that can be affected by both consumption (source function) and pollution (sink function). Therefore, both types of elementary flows (water consumption and emissions) should be linked to a damage indicator for freshwater as a resource. Land use is also identified as a potential stressor to freshwater resources by altering runoff, infiltration, and erosion processes as well as evapotranspiration. It is suggested to use the concept of recovery period to operationalize this framework: when the recovery period lasts longer than a given period of time, impacts are considered to be irreversible and fall into the concern of freshwater resources protection (i.e., affecting future generations), while short-term impacts effect the AoP ecosystem quality and human health directly. It is shown that it is relevant to include this concept in the impact assessment stage in order to discriminate the long-term from the short-term impacts, as some dynamic fate models already do. Conclusions: This framework provides a solid basis for the consistent development of future LCIA methods for freshwater resources, thereby capturing the potential long-term impacts that could warn decision makers about potential safe water supply issues in the future

The Impact of Minority Faith on the Experience of Mental Health Services: The Perspectives of Devotees of Earth Religions

In response to an identified need in the psychological literature for research on minority religion, especially earth-centered religion, this dissertation was developed to 1) present an overview of the three main branches of contemporary earth religion, 2) illuminate the realities of minority religious identity in the United States of America, 3) collect data regarding the demographic and identity variables of devotees of earth centered religion, and 4) solicit feedback from the earth religious community regarding its understanding of psychological distress, preferred ways of coping with distress, and perceptions and experiences of professional mental health services. A total of 64 self-identified devotees of earth-centered faith completed an online questionnaire about their identity variables, experiences of psychological distress, ways of understanding distress, and experiences, perceptions, and fears pertaining to mental health services. The questionnaire was developed by the researcher based upon a literature review and consultation of the National Council of Schools and Programs of Professional Psychology\u27s developmental achievement levels in diversity. Descriptive and statistical findings pertaining to this religious population are detailed. Additionally, clinical and research implications of the results, as well as limitations and strengths of the current study are identified and discussed

Experimental and Analytical Study of Helical Cross-Flow Turbines for a Tidal Micropower Generation System

Thesis (Master's)--University of Washington, 2012This study investigates the feasibility of a micro-scale tidal hydrokinetic generator to power autonomous oceanographic instrumentation, with emphasis on turbine design and performance. This type of "micropower" system is intended to provide continuous power on the order of 20 Watts. System components are reviewed and include turbine, electrical generator, gearbox, controller, converter, and battery bank. A steady-state model predicts system energy storage and power output in a mixed, mainly semidiurnal tidal regime with peak currents of 1.5 m/s. Among several turbine designs reviewed, a helical cross-flow turbine is selected, due to its self-start capability, ability to accept inflow from any direction, and power performance. Parameters impacting helical turbine design include radius, blade profile and pitch, aspect ratio, helical pitch, number of blades, solidity ratio, blade wrap ratio, strut design, and shaft diameter. The performance trade-offs of each are compared. A set of three prototype-scale turbines (two three-bladed designs, with 15% and 30% solidity, and a four-bladed design with 30% solidity and higher helical pitch) and several strut and shaft configurations were fabricated and tested in a water flume capable of flow rates up to 0.8 m/s. Tests included performance characterization of the rotating turbines from freewheel to stall, static torque characterization as a function of azimuthal angle, performance degradation associated with inclination angles up to 10° from vertical, and stream-wise wake velocity profiles. A four-bladed turbine with 60° helical pitch, 30% solidity, and circular plate "end cap" provided the best performance; this design attained efficiency of 24% in 0.8 m/s flow and experienced smaller performance reductions for tilted orientations relative to other variants. Maximum turbine efficiency increased with increased flume velocity. A free-vortex model was modified to simulate the helical turbine performance. Model results were compared to experimental data for various strut design and inflow velocities, and performance was extrapolated to higher flume velocities and a full-scale turbine (0.7 m2 relative to 0.04 m2 in flume tests). The model predicts experimental trends correctly but deviates from experimental values for some conditions, indicating the need for further study of secondary effects for a high chord-to-radius ratio turbine

Comparison of Deep Learning and Feature Matching Methods for Homography Estimation

Planar homography estimation is foundational to many computer vision problems, such as Simultaneous Localization and Mapping (SLAM) and Augmented Reality (AR). However, conditions of high variance confound even the state-of-the-art algorithms. In this report, we analyze the performance of two recently published methods using Convolutional Neural Networks (CNNs) that are meant to replace the more traditional feature-matching based approaches to the estimation of homography. Our evaluation of the CNN based methods focuses particularly on measuring the performance under conditions of significant noise, illumination shift, and occlusion. We also measure the benefits of training CNNs to varying degrees of noise. Additionally, we compare the effect of using color images instead of grayscale images for inputs to CNNs. Finally, we compare the results against baseline feature-matching based homography estimation methods using SIFT, SURF, and ORB. We find that CNNs can be trained to be more robust against noise, but at a small cost to accuracy in the noiseless case. Additionally, CNNs perform significantly better in conditions of extreme variance than their feature-matching based counterparts. With regard to color inputs, we conclude that with no change in the CNN architecture to take advantage of the additional information in the color planes, the difference in performance using color inputs or grayscale inputs is negligible. About the CNNs trained with noise-corrupted inputs, we show that training a CNN to a specific magnitude of noise leads to a “Goldilocks Zone” with regard to the noise levels where that CNN performs best