Dr. Dallas H Snider

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.

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.