Dr. Dallas H Snider

Software defined networks and network function virtualization are providing much needed agility to network and system administrators attempting to meet everchanging demands from their stakeholders. However, there exists a need for tools to assist in the configuration, deployment, testing and knowledge transfer of these networks and their components. In this paper, we present our plans to develop a set of open source tools to assist with the aforementioned tasks. The goal is to apply existing disparate pieces of technology so they may work together to assist with the management of virtual network environments.

There are more than 1000 “Free Clinics” in the United States that serve uninsured and underserved patients. The majority treat chronic diseases, such as type 2 diabetes and
its co-morbidities. Because free clinics are not eligible for federal reimbursement for electronic health record use, very few are able to track and manage chronic diseases
electronically. Historically, clinicians have tracked metabolic and other matrices with paper forms and spreadsheets. Electronic disease registries are tools that support chronic diseases, such as type 2 diabetes and hypertension. We present the results of our web-based registry to support diabetes care in free medical clinics.

A process has been developed to transfer network intrusion data captured by Fail2ban to an adaptive enterprise intrusion detection and prevention system. The process involves software agents that we have created that are interconnected to a central behavior analysis database service where each software agent records attack meta-information collected during previous intrusion attempts. These distributed agents are the first phase of an overall plan to create a smarter network defense system through the collection and analysis of network signatures generated by real security threats. The central database to which the agents report warehouses and analyzes the meta-information collected by the interconnected agents. The agents can then utilize both instantaneous and historical data by integrating rules derived from the data collection and analysis process into intrusion prevention policies. The final result will be a modular and scalable network defense system that should be more responsive and adaptable to imminent threats.

With the increase in smart, LEED-certified buildings there comes an increase in the amount of time-series data generated by the sensor networks within these buildings. Extracting useful information from the sensor network data can pose a challenge. While diurnal and seasonal patterns of electrical demand are well known from traditional metering systems, smart building sensor networks can provide insight into abnormalities or previously unknown patterns in electrical demand. In this paper, we demonstrate how to mine the data for these unknowns through the analysis of the frequency components of the time-series electrical demand data. The data for this study was collected from an LEED-certified building over 12 consecutive months with separate feeds for the electrical demand from the heating, A/C, ventilation, lighting, and miscellaneous systems. We employed Fourier methods to transform the data from the time domain to the frequency domain and then used similarity measures to look for similarities and outliers among the differing systems.

The development of security assurance cases has been touted as one way to improve the security of mission-critical software. However, security assurance cases are not easy to write, communicate, or introduce into the software development process. We describe a two-phase approach to the development of security assurance cases: a first phase in which critical vulnerabilities are identified, and a second phase in which documentation is produced demonstrating that these vulnerabilities have been addressed. Concept maps, an easily understood graphical representation of conceptual knowledge are used to construct the security assurance case. The current work contains a description of a pilot study in the use of this approach.

We live in an environment where an increasing number of everyday objects are becoming network enabled and generating copious amounts of data. These networked objects are forming an Internet of Things (IoT) where the growth of data for analysis and discovery is accelerating at a rate that will prohibit efficient processing by current hardware and software. With the increasing use of smart grid technologies, the electrical energy sector is not immune to these big data problems. While there has been extensive research and industry development in big data software and hardware products for processing large volumes of data, the smart grid IoT is creating new problems that must be solved.

Today's warfighter is increasingly dependent on networked systems and information from unmanned aerial vehicles to provide up-to-the-minute conditions on the battlefield; therefore the network must continually perform at optimum levels. One of the problems encountered in complex air-to-ground networks is that all possible hardware, software and network configurations that will be encountered in the field cannot be pretested; therefore there is a need to provide a method for studying the interaction among diverse hardware and software components and identifying potential network bottlenecks in air-to-ground networks and their causes before they become critical. In this paper, we demonstrate our accomplishments in building a robust and scalable simulation of an air-to-ground network environment based on wired and wireless network emulation using widely available software tools. We will also present our design for data capture and evaluation that support hypothetical, what-if scenarios for testing network performance bottlenecks.